Automated clinical documentation system and method

ABSTRACT

A method, computer program product, and computing system for tracking encounter participants is executed on a computing device and includes obtaining encounter information of a patient encounter, wherein the encounter information includes machine vision encounter information obtained via one or more machine vision systems. The machine vision encounter information is processed to identify one or more humanoid shapes.

RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.16/058,951, filed on 8 Aug. 2018; which claims the benefit of thefollowing U.S. Provisional Application Nos. 62/543,762, filed on 10 Aug.2017; and 62/638,809, filed on 5 Mar. 2018; their entire contents ofwhich are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to documentation systems and methods and, moreparticularly, to automated clinical documentation systems and methods.

BACKGROUND

As is known in the art, clinical documentation is the creation ofmedical records and documentation that details the medical history ofmedical patients. As would be expected, traditional clinicaldocumentation includes various types of data, examples of which mayinclude but are not limited to paper-based documents and transcripts, aswell as various images and diagrams.

As the world moved from paper-based content to digital content, clinicaldocumentation also moved in that direction, where medical records anddocumentation were gradually transitioned from stacks of papergeographically-dispersed across multiple locations/institutions toconsolidated and readily accessible digital content.

SUMMARY OF DISCLOSURE

Invention #12

In one implementation, a computer-implemented method for trackingencounter participants is executed on a computing device and includesobtaining encounter information of a patient encounter, wherein theencounter information includes machine vision encounter informationobtained via one or more machine vision systems. The machine visionencounter information is processed to identify one or more humanoidshapes.

One or more of the following features may be included. Processing themachine vision encounter information to identify one or more humanoidshapes may include tracking the movement of the one or more humanoidshapes within a monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include adding a newhumanoid shape to the one or more humanoid shapes when the new humanoidshape enters the monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include removing anexisting humanoid shape from the one or more humanoid shapes when theexisting humanoid shape leaves the monitored space. One or more audiorecording beams may be steered toward the one or more humanoid shapes tocapture audio encounter information, wherein the audio encounterinformation is included within the encounter information. The encounterinformation may be processed to generate an encounter transcript. Atleast a portion of the encounter transcript may be processed to populateat least a portion of a medical record associated with the patientencounter. The one or more machine vision systems may include one ormore of: a visible energy imaging system (e.g., an RGB imaging system);an invisible energy imaging system (e.g., infrared, ultraviolet, laserimaging system); an X-ray imaging system; a SONAR imaging .system; aRADAR imaging system; and a thermal imaging system.

In another implementation, a computer program product resides on acomputer readable medium and has a plurality of instructions stored onit. When executed by a processor, the instructions cause the processorto perform operations including obtaining encounter information of apatient encounter, wherein the encounter information includes machinevision encounter information obtained via one or more machine visionsystems. The machine vision encounter information is processed toidentify one or more humanoid shapes.

One or more of the following features may be included. Processing themachine vision encounter information to identify one or more humanoidshapes may include tracking the movement of the one or more humanoidshapes within a monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include adding a newhumanoid shape to the one or more humanoid shapes when the new humanoidshape enters the monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include removing anexisting humanoid shape from the one or more humanoid shapes when theexisting humanoid shape leaves the monitored space. One or more audiorecording beams may be steered toward the one or more humanoid shapes tocapture audio encounter information, wherein the audio encounterinformation is included within the encounter information. The encounterinformation may be processed to generate an encounter transcript. Atleast a portion of the encounter transcript may be processed to populateat least a portion of a medical record associated with the patientencounter. The one or more machine vision systems may include one ormore of: a visible energy imaging system (e.g., an RGB imaging system);an invisible energy imaging system (e.g., infrared, ultraviolet, laserimaging system); an X-ray imaging system; a SONAR imaging .system; aRADAR imaging system; and a thermal imaging system.

In another implementation, a computing system includes a processor andmemory is configured to perform operations including obtaining encounterinformation of a patient encounter, wherein the encounter informationincludes machine vision encounter information obtained via one or moremachine vision systems. The machine vision encounter information isprocessed to identify one or more humanoid shapes.

One or more of the following features may be included. Processing themachine vision encounter information to identify one or more humanoidshapes may include tracking the movement of the one or more humanoidshapes within a monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include adding a newhumanoid shape to the one or more humanoid shapes when the new humanoidshape enters the monitored space. Tracking the movement of the one ormore humanoid shapes within the monitored space may include removing anexisting humanoid shape from the one or more humanoid shapes when theexisting humanoid shape leaves the monitored space. One or more audiorecording beams may be steered toward the one or more humanoid shapes tocapture audio encounter information, wherein the audio encounterinformation is included within the encounter information. The encounterinformation may be processed to generate an encounter transcript. Atleast a portion of the encounter transcript may be processed to populateat least a portion of a medical record associated with the patientencounter. The one or more machine vision systems may include one ormore of: a visible energy imaging system (e.g., an RGB imaging system);an invisible energy imaging system (e.g., infrared, ultraviolet, laserimaging system); an X-ray imaging system; a SONAR imaging .system; aRADAR imaging system; and a thermal imaging system.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of an automated clinical documentationcompute system and an automated clinical documentation process coupledto a distributed computing network;

FIG. 2 is a diagrammatic view of a modular ACD system incorporating theautomated clinical documentation compute system of FIG. 1;

FIG. 3 is a diagrammatic view of a mixed-media ACD device includedwithin the modular ACD system of FIG. 2;

FIG. 4 is a flow chart of one implementation of the automated clinicaldocumentation process of FIG. 1;

FIG. 5 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 6 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 7 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 8 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 9 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 10 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 11 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 12 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIGS. 13A-13E are diagrammatic views of the encounter transcript andsemantic frames as generated by the automated clinical documentationprocess of FIG. 1;

FIGS. 14A-14B are diagrammatic views of a medical record as populated bythe automated clinical documentation process of FIG. 1;

FIG. 15 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 16 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 17 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 18 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 19 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 20 is a diagrammatic view of an ACD media player for use with theautomated clinical documentation process of FIG. 1;

FIG. 21 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 22 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1;

FIG. 23 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1; and

FIG. 24 is a flow chart of another implementation of the automatedclinical documentation process of FIG. 1.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS System Overview:

Referring to FIG. 1, there is shown automated clinical documentationprocess 10. As will be discussed below in greater detail, automatedclinical documentation process 10 may be configured to automate thecollection and processing of clinical encounter information togenerate/store/distribute medical records.

Automated clinical documentation process 10 may be implemented as aserver-side process, a client-side process, or a hybridserver-side/client-side process. For example, automated clinicaldocumentation process 10 may be implemented as a purely server-sideprocess via automated clinical documentation process 10 s.Alternatively, automated clinical documentation process 10 may beimplemented as a purely client-side process via one or more of automatedclinical documentation process 10 c 1, automated clinical documentationprocess 10 c 2, automated clinical documentation process 10 c 3, andautomated clinical documentation process 10 c 4. Alternatively still,automated clinical documentation process 10 may be implemented as ahybrid server-side/client-side process via automated clinicaldocumentation process 10 s in combination with one or more of automatedclinical documentation process 10 c 1, automated clinical documentationprocess 10 c 2, automated clinical documentation process 10 c 3, andautomated clinical documentation process 10 c 4.

Accordingly, automated clinical documentation process 10 as used in thisdisclosure may include any combination of automated clinicaldocumentation process 10 s, automated clinical documentation process 10c 1, automated clinical documentation process 10 c 2, automated clinicaldocumentation process 10 c 3, and automated clinical documentationprocess 10 c 4.

Automated clinical documentation process 10 s may be a serverapplication and may reside on and may be executed by automated clinicaldocumentation (ACD) compute system 12, which may be connected to network14 (e.g., the Internet or a local area network). ACD compute system 12may include various components, examples of which may include but arenot limited to: a personal computer, a server computer, a series ofserver computers, a mini computer, a mainframe computer, one or moreNetwork Attached Storage (NAS) systems, one or more Storage Area Network(SAN) systems, one or more Platform as a Service (PaaS) systems, one ormore Infrastructure as a Service (IaaS) systems, one or more Software asa Service (SaaS) systems, a cloud-based computational system, and acloud-based storage platform.

As is known in the art, a SAN may include one or more of a personalcomputer, a server computer, a series of server computers, a minicomputer, a mainframe computer, a RAID device and a NAS system. Thevarious components of ACD compute system 12 may execute one or moreoperating systems, examples of which may include but are not limited to:Microsoft Windows Server™; Redhat Linux™, Unix, or a custom operatingsystem, for example.

The instruction sets and subroutines of automated clinical documentationprocess 10 s, which may be stored on storage device 16 coupled to ACDcompute system 12, may be executed by one or more processors (not shown)and one or more memory architectures (not shown) included within ACDcompute system 12. Examples of storage device 16 may include but are notlimited to: a hard disk drive; a RAID device; a random access memory(RAM); a read-only memory (ROM); and all forms of flash memory storagedevices.

Network 14 may be connected to one or more secondary networks (e.g.,network 18), examples of which may include but are not limited to: alocal area network; a wide area network; or an intranet, for example.

Various IO requests (e.g. IO request 20) may be sent from automatedclinical documentation process 10 s, automated clinical documentationprocess 10 c 1, automated clinical documentation process 10 c 2,automated clinical documentation process 10 c 3 and/or automatedclinical documentation process 10 c 4 to ACD compute system 12. Examplesof IO request 20 may include but are not limited to data write requests(i.e. a request that content be written to ACD compute system 12) anddata read requests (i.e. a request that content be read from ACD computesystem 12).

The instruction sets and subroutines of automated clinical documentationprocess 10 c 1, automated clinical documentation process 10 c 2,automated clinical documentation process 10 c 3 and/or automatedclinical documentation process 10 c 4, which may be stored on storagedevices 20, 22, 24, 26 (respectively) coupled to ACD client electronicdevices 28, 30, 32, 34 (respectively), may be executed by one or moreprocessors (not shown) and one or more memory architectures (not shown)incorporated into ACD client electronic devices 28, 30, 32, 34(respectively). Storage devices 20, 22, 24, 26 may include but are notlimited to: hard disk drives; optical drives; RAID devices; randomaccess memories (RAM); read-only memories (ROM), and all forms of flashmemory storage devices. Examples of ACD client electronic devices 28,30, 32, 34 may include, but are not limited to, personal computingdevice 28 (e.g., a smart phone, a personal digital assistant, a laptopcomputer, a notebook computer, and a desktop computer), audio inputdevice 30 (e.g., a handheld microphone, a lapel microphone, an embeddedmicrophone (such as those embedded within eyeglasses, smart phones,tablet computers and/or watches) and an audio recording device), displaydevice 32 (e.g., a tablet computer, a computer monitor, and a smarttelevision), machine vision input device 34 (e.g., an RGB imagingsystem, an infrared imaging system, an ultraviolet imaging system, alaser imaging system, a SONAR imaging system, a RADAR imaging system,and a thermal imaging system), a hybrid device (e.g., a single devicethat includes the functionality of one or more of the above-referencesdevices; not shown), an audio rendering device (e.g., a speaker system,a headphone system, or an earbud system; not shown), various medicaldevices (e.g., medical imaging equipment, heart monitoring machines,body weight scales, body temperature thermometers, and blood pressuremachines; not shown), and a dedicated network device (not shown).

Users 36, 38, 40, 42 may access ACD compute system 12 directly throughnetwork 14 or through secondary network 18. Further, ACD compute system12 may be connected to network 14 through secondary network 18, asillustrated with link line 44.

The various ACD client electronic devices (e.g., ACD client electronicdevices 28, 30, 32, 34) may be directly or indirectly coupled to network14 (or network 18). For example, personal computing device 28 is showndirectly coupled to network 14 via a hardwired network connection.Further, machine vision input device 34 is shown directly coupled tonetwork 18 via a hardwired network connection. Audio input device 30 isshown wirelessly coupled to network 14 via wireless communicationchannel 46 established between audio input device 30 and wireless accesspoint (i.e., WAP) 48, which is shown directly coupled to network 14. WAP48 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,Wi-Fi, and/or Bluetooth device that is capable of establishing wirelesscommunication channel 46 between audio input device 30 and WAP 48.Display device 32 is shown wirelessly coupled to network 14 via wirelesscommunication channel 50 established between display device 32 and WAP52, which is shown directly coupled to network 14.

The various ACD client electronic devices (e.g., ACD client electronicdevices 28, 30, 32, 34) may each execute an operating system, examplesof which may include but are not limited to Microsoft Windows™, AppleMacintosh™, Redhat Linux™, or a custom operating system, wherein thecombination of the various ACD client electronic devices (e.g., ACDclient electronic devices 28, 30, 32, 34) and ACD compute system 12 mayform modular ACD system 54.

The Automated Clinical Documentation System:

Invention #6

Referring also to FIG. 2, there is shown a simplified exemplaryembodiment of modular ACD system 54 that is configured to automateclinical documentation. Modular ACD system 54 may include: machinevision system 100 configured to obtain machine vision encounterinformation 102 concerning a patient encounter; audio recording system104 configured to obtain audio encounter information 106 concerning thepatient encounter; and a compute system (e.g., ACD compute system 12)configured to receive machine vision encounter information 102 and audioencounter information 106 from machine vision system 100 and audiorecording system 104 (respectively). Modular ACD system 54 may alsoinclude: display rendering system 108 configured to render visualinformation 110; and audio rendering system 112 configured to renderaudio information 114, wherein ACD compute system 12 may be configuredto provide visual information 110 and audio information 114 to displayrendering system 108 and audio rendering system 112 (respectively).

Example of machine vision system 100 may include but are not limited to:one or more ACD client electronic devices (e.g., ACD client electronicdevice 34, examples of which may include but are not limited to an RGBimaging system, an infrared imaging system, a ultraviolet imagingsystem, a laser imaging system, a SONAR imaging system, a RADAR imagingsystem, and a thermal imaging system). Examples of audio recordingsystem 104 may include but are not limited to: one or more ACD clientelectronic devices (e.g., ACD client electronic device 30, examples ofwhich may include but are not limited to a handheld microphone, a lapelmicrophone, an embedded microphone (such as those embedded withineyeglasses, smart phones, tablet computers and/or watches) and an audiorecording device). Examples of display rendering system 108 may includebut are not limited to: one or more ACD client electronic devices (e.g.,ACD client electronic device 32, examples of which may include but arenot limited to a tablet computer, a computer monitor, and a smarttelevision). Examples of audio rendering system 112 may include but arenot limited to: one or more ACD client electronic devices (e.g., audiorendering device 116, examples of which may include but are not limitedto a speaker system, a headphone system, and an earbud system).

As will be discussed below in greater detail, ACD compute system 12 maybe configured to access one or more datasources 118 (e.g., plurality ofindividual datasources 120, 122, 124, 126, 128), examples of which mayinclude but are not limited to one or more of a user profile datasource,a voice print datasource, a voice characteristics datasource (e.g., foradapting the automated speech recognition models), a face printdatasource, a humanoid shape datasource, an utterance identifierdatasource, a wearable token identifier datasource, an interactionidentifier datasource, a medical conditions symptoms datasource, aprescriptions compatibility datasource, a medical insurance coveragedatasource, and a home healthcare datasource. While in this particularexample, five different examples of datasources 118, are shown, this isfor illustrative purposes only and is not intended to be a limitation ofthis disclosure, as other configurations are possible and are consideredto be within the scope of this disclosure.

As will be discussed below in greater detail, modular ACD system 54 maybe configured to monitor a monitored space (e.g., monitored space 130)in a clinical environment, wherein examples of this clinical environmentmay include but are not limited to: a doctor's office, a medicalfacility, a medical practice, a medical lab, an urgent care facility, amedical clinic, an emergency room, an operating room, a hospital, a longterm care facility, a rehabilitation facility, a nursing home, and ahospice facility. Accordingly, an example of the above-referencedpatient encounter may include but is not limited to a patient visitingone or more of the above-described clinical environments (e.g., adoctor's office, a medical facility, a medical practice, a medical lab,an urgent care facility, a medical clinic, an emergency room, anoperating room, a hospital, a long term care facility, a rehabilitationfacility, a nursing home, and a hospice facility).

Machine vision system 100 may include a plurality of discrete machinevision systems when the above-described clinical environment is largeror a higher level of resolution is desired. As discussed above, examplesof machine vision system 100 may include but are not limited to: one ormore ACD client electronic devices (e.g., ACD client electronic device34, examples of which may include but are not limited to an RGB imagingsystem, an infrared imaging system, an ultraviolet imaging system, alaser imaging system, a SONAR imaging system, a RADAR imaging system,and a thermal imaging system). Accordingly, machine vision system 100may include one or more of each of an RGB imaging system, an infraredimaging systems, an ultraviolet imaging systems, a laser imaging system,a SONAR imaging system, a RADAR imaging system, and a thermal imagingsystem.

Audio recording system 104 may include a plurality of discrete audiorecording systems when the above-described clinical environment islarger or a higher level of resolution is desired. As discussed above,examples of audio recording system 104 may include but are not limitedto: one or more ACD client electronic devices (e.g., ACD clientelectronic device 30, examples of which may include but are not limitedto a handheld microphone, a lapel microphone, an embedded microphone(such as those embedded within eyeglasses, smart phones, tabletcomputers and/or watches) and an audio recording device). Accordingly,audio recording system 104 may include one or more of each of a handheldmicrophone, a lapel microphone, an embedded microphone (such as thoseembedded within eyeglasses, smart phones, tablet computers and/orwatches) and an audio recording device.

Display rendering system 108 may include a plurality of discrete displayrendering systems when the above-described clinical environment islarger or a higher level of resolution is desired. As discussed above,examples of display rendering system 108 may include but are not limitedto: one or more ACD client electronic devices (e.g., ACD clientelectronic device 32, examples of which may include but are not limitedto a tablet computer, a computer monitor, and a smart television).Accordingly, display rendering system 108 may include one or more ofeach of a tablet computer, a computer monitor, and a smart television.

Audio rendering system 112 may include a plurality of discrete audiorendering systems when the above-described clinical environment islarger or a higher level of resolution is desired. As discussed above,examples of audio rendering system 112 may include but are not limitedto: one or more ACD client electronic devices (e.g., audio renderingdevice 116, examples of which may include but are not limited to aspeaker system, a headphone system, or an earbud system). Accordingly,audio rendering system 112 may include one or more of each of a speakersystem, a headphone system, or an earbud system.

ACD compute system 12 may include a plurality of discrete computesystems. As discussed above, ACD compute system 12 may include variouscomponents, examples of which may include but are not limited to: apersonal computer, a server computer, a series of server computers, amini computer, a mainframe computer, one or more Network AttachedStorage (NAS) systems, one or more Storage Area Network (SAN) systems,one or more Platform as a Service (PaaS) systems, one or moreInfrastructure as a Service (IaaS) systems, one or more Software as aService (SaaS) systems, a cloud-based computational system, and acloud-based storage platform. Accordingly, ACD compute system 12 mayinclude one or more of each of a personal computer, a server computer, aseries of server computers, a mini computer, a mainframe computer, oneor more Network Attached Storage (NAS) systems, one or more Storage AreaNetwork (SAN) systems, one or more Platform as a Service (PaaS) systems,one or more Infrastructure as a Service (IaaS) systems, one or moreSoftware as a Service (SaaS) systems, a cloud-based computationalsystem, and a cloud-based storage platform.

Referring also to FIG. 3, audio recording system 104 may includedirectional microphone array 200 having a plurality of discretemicrophone assemblies. For example, audio recording system 104 mayinclude a plurality of discrete audio acquisition devices (e.g., audioacquisition devices 202, 204, 206, 208, 210, 212, 214, 216, 218) thatmay form microphone array 200. As will be discussed below in greaterdetail, modular ACD system 54 may be configured to form one or moreaudio recording beams (e.g., audio recording beams 220, 222, 224) viathe discrete audio acquisition devices (e.g., audio acquisition devices202, 204, 206, 208, 210, 212, 214, 216, 218) included within audiorecording system 104.

For example, modular ACD system 54 may be further configured to steerthe one or more audio recording beams (e.g., audio recording beams 220,222, 224) toward one or more encounter participants (e.g., encounterparticipants 226, 228, 230) of the above-described patient encounter.Examples of the encounter participants (e.g., encounter participants226, 228, 230) may include but are not limited to: medical professionals(e.g., doctors, nurses, physician's assistants, lab technicians,physical therapists, scribes (e.g., a transcriptionist) and/or staffmembers involved in the patient encounter), patients (e.g., people thatare visiting the above-described clinical environments for the patientencounter), and third parties (e.g., friends of the patient, relativesof the patient and/or acquaintances of the patient that are involved inthe patient encounter).

Accordingly, modular ACD system 54 and/or audio recording system 104 maybe configured to utilize one or more of the discrete audio acquisitiondevices (e.g., audio acquisition devices 202, 204, 206, 208, 210, 212,214, 216, 218) to form an audio recording beam. For example, modular ACDsystem 54 and/or audio recording system 104 may be configured to utilizeaudio acquisition device 210 to form audio recording beam 220, thusenabling the capturing of audio (e.g., speech) produced by encounterparticipant 226 (as audio acquisition device 210 is pointed to (i.e.,directed toward) encounter participant 226). Additionally, modular ACDsystem 54 and/or audio recording system 104 may be configured to utilizeaudio acquisition devices 204, 206 to form audio recording beam 222,thus enabling the capturing of audio (e.g., speech) produced byencounter participant 228 (as audio acquisition devices 204, 206 arepointed to (i.e., directed toward) encounter participant 228).Additionally, modular ACD system 54 and/or audio recording system 104may be configured to utilize audio acquisition devices 212, 214 to formaudio recording beam 224, thus enabling the capturing of audio (e.g.,speech) produced by encounter participant 230 (as audio acquisitiondevices 212, 214 are pointed to (i.e., directed toward) encounterparticipant 230). Further, modular ACD system 54 and/or audio recordingsystem 104 may be configured to utilize null-steering precoding tocancel interference between speakers and/or noise.

As is known in the art, null-steering precoding is a method of spatialsignal processing by which a multiple antenna transmitter may nullmultiuser interference signals in wireless communications, whereinnull-steering precoding may mitigate the impact off background noise andunknown user interference.

In particular, null-steering precoding may be a method of beamformingfor narrowband signals that may compensate for delays of receivingsignals from a specific source at different elements of an antennaarray. In general and to improve performance of the antenna array, inincoming signals may be summed and averaged, wherein certain signals maybe weighted and compensation may be made for signal delays.

Machine vision system 100 and audio recording system 104 may bestand-alone devices (as shown in FIG. 2). Additionally/alternatively,machine vision system 100 and audio recording system 104 may be combinedinto one package to form mixed-media ACD device 232. For example,mixed-media ACD device 232 may be configured to be mounted to astructure (e.g., a wall, a ceiling, a beam, a column) within theabove-described clinical environments (e.g., a doctor's office, amedical facility, a medical practice, a medical lab, an urgent carefacility, a medical clinic, an emergency room, an operating room, ahospital, a long term care facility, a rehabilitation facility, anursing home, and a hospice facility), thus allowing for easyinstallation of the same. Further, modular ACD system 54 may beconfigured to include a plurality of mixed-media ACD devices (e.g.,mixed-media ACD device 232) when the above-described clinicalenvironment is larger or a higher level of resolution is desired.

The Mixed-Media Automated Clinical Documentation Device:

Invention #11

Modular ACD system 54 may be further configured to steer the one or moreaudio recording beams (e.g., audio recording beams 220, 222, 224) towardone or more encounter participants (e.g., encounter participants 226,228, 230) of the patient encounter based, at least in part, upon machinevision encounter information 102. As discussed above, mixed-media ACDdevice 232 (and machine vision system 100/audio recording system 104included therein) may be configured to monitor one or more encounterparticipants (e.g., encounter participants 226, 228, 230) of a patientencounter.

Specifically and as will be discussed below in greater detail, machinevision system 100 (either as a stand-alone system or as a component ofmixed-media ACD device 232) may be configured to detect humanoid shapeswithin the above-described clinical environments (e.g., a doctor'soffice, a medical facility, a medical practice, a medical lab, an urgentcare facility, a medical clinic, an emergency room, an operating room, ahospital, a long term care facility, a rehabilitation facility, anursing home, and a hospice facility). And when these humanoid shapesare detected by machine vision system 100, modular ACD system 54 and/oraudio recording system 104 may be configured to utilize one or more ofthe discrete audio acquisition devices (e.g., audio acquisition devices202, 204, 206, 208, 210, 212, 214, 216, 218) to form an audio recordingbeam (e.g., audio recording beams 220, 222, 224) that is directed towardeach of the detected humanoid shapes (e.g., encounter participants 226,228, 230).

As discussed above, ACD compute system 12 may be configured to receivemachine vision encounter information 102 and audio encounter information106 from machine vision system 100 and audio recording system 104(respectively); and may be configured to provide visual information 110and audio information 114 to display rendering system 108 and audiorendering system 112 (respectively). Depending upon the manner in whichmodular ACD system 54 (and/or mixed-media ACD device 232) is configured,ACD compute system 12 may be included within mixed-media ACD device 232or external to mixed-media ACD device 232.

The Automated Clinical Documentation Process:

As discussed above, ACD compute system 12 may execute all or a portionof automated clinical documentation process 10, wherein the instructionsets and subroutines of automated clinical documentation process 10(which may be stored on one or more of e.g., storage devices 16, 20, 22,24, 26) may be executed by ACD compute system 12 and/or one or more ofACD client electronic devices 28, 30, 32, 34.

Invention #1

As discussed above, automated clinical documentation process 10 may beconfigured to automate the collection and processing of clinicalencounter information to generate/store/distribute medical records.Accordingly and referring also to FIG. 4, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office). Automated clinical documentation process 10 mayfurther be configured to process 302 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) to generate an encounter transcript (e.g., encountertranscript 234), wherein automated clinical documentation process 10 maythen process 304 at least a portion of the encounter transcript (e.g.,encounter transcript 234) to populate at least a portion of a medicalrecord (e.g., medical record 236) associated with the patient encounter(e.g., the visit to the doctor's office). Encounter transcript 234and/or medical record 236 may be reviewed by a medical professionalinvolved with the patient encounter (e.g., a visit to a doctor's office)to determine the accuracy of the same and/or make corrections to thesame.

For example, a scribe involved with (or assigned to) the patientencounter (e.g., a visit to a doctor's office) may review encountertranscript 234 and/or medical record 236 to confirm that the same wasaccurate and/or make corrections to the same. In the event thatcorrections are made to encounter transcript 234 and/or medical record236, automated clinical documentation process 10 may utilize thesecorrections for training/tuning purposes (e.g., to adjust the variousprofiles associated the participants of the patient encounter) toenhance the future accuracy/efficiency/performance of automated clinicaldocumentation process 10.

Alternatively/additionally, a doctor involved with the patient encounter(e.g., a visit to a doctor's office) may review encounter transcript 234and/or medical record 236 to confirm that the same was accurate and/ormake corrections to the same. In the event that corrections are made toencounter transcript 234 and/or medical record 236, automated clinicaldocumentation process 10 may utilize these corrections fortraining/tuning purposes (e.g., to adjust the various profilesassociated the participants of the patient encounter) to enhance thefuture accuracy/efficiency/performance of automated clinicaldocumentation process 10.

For example, assume that a patient (e.g., encounter participant 228)visits a clinical environment (e.g., a doctor's office) because they donot feel well. They have a headache, fever, chills, a cough, and somedifficulty breathing. In this particular example, a monitored space(e.g., monitored space 130) within the clinical environment (e.g., thedoctor's office) may be outfitted with machine vision system 100configured to obtain machine vision encounter information 102 concerningthe patient encounter (e.g., encounter participant 228 visiting thedoctor's office) and audio recording system 104 configured to obtainaudio encounter information 106 concerning the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) via one or moreaudio sensors (e.g., audio acquisition devices 202, 204, 206, 208, 210,212, 214, 216, 218).

As discussed above, machine vision system 100 may include a plurality ofdiscrete machine vision systems if the monitored space (e.g., monitoredspace 130) within the clinical environment (e.g., the doctor's office)is larger or a higher level of resolution is desired, wherein examplesof machine vision system 100 may include but are not limited to: an RGBimaging system, an infrared imaging system, an ultraviolet imagingsystem, a laser imaging system, a SONAR imaging system, a RADAR imagingsystem, and a thermal imaging system. Accordingly and in certaininstances/embodiments, machine vision system 100 may include one or moreof each of an RGB imaging system, an infrared imaging system, anultraviolet imaging system, a laser imaging system, a SONAR imagingsystem, a RADAR imaging system, and a thermal imaging system positionedthroughout monitored space 130, wherein each of these systems may beconfigured to provide data (e.g., machine vision encounter information102) to ACD compute system 12 and/or modular ACD system 54.

As also discussed above, audio recording system 104 may include aplurality of discrete audio recording systems if the monitored space(e.g., monitored space 130) within the clinical environment (e.g., thedoctor's office) is larger or a higher level of resolution is desired,wherein examples of audio recording system 104 may include but are notlimited to: a handheld microphone, a lapel microphone, an embeddedmicrophone (such as those embedded within eyeglasses, smart phones,tablet computers and/or watches) and an audio recording device.Accordingly and in certain instances/embodiments, audio recording system104 may include one or more of each of a handheld microphone, a lapelmicrophone, an embedded microphone (such as those embedded withineyeglasses, smart phones, tablet computers and/or watches) and an audiorecording device positioned throughout monitored space 130, wherein eachof these microphones/devices may be configured to provide data (e.g.,audio encounter information 106) to ACD compute system 12 and/or modularACD system 54.

Since machine vision system 100 and audio recording system 104 may bepositioned throughout monitored space 130, all of the interactionsbetween medical professionals (e.g., encounter participant 226),patients (e.g., encounter participant 228) and third parties (e.g.,encounter participant 230) that occur during the patient encounter(e.g., encounter participant 228 visiting the doctor's office) withinthe monitored space (e.g., monitored space 130) of the clinicalenvironment (e.g., the doctor's office) may bemonitored/recorded/processed. Accordingly, a patient “check-in” areawithin monitored space 130 may be monitored to obtain encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) during this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice). Further, various rooms within monitored space 130 may bemonitored to obtain encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) duringthese various portions of the patient encounter (e.g., while meetingwith the doctor, while vital signs and statistics are obtained, andwhile imaging is performed). Further, a patient “check-out” area withinmonitored space 130 may be monitored to obtain encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) during this post-visit portion of the patient encounter(e.g., encounter participant 228 visiting the doctor's office).Additionally and via machine vision encounter information 102, visualspeech recognition (via visual lip reading functionality) may beutilized by automated clinical documentation process 10 to furthereffectuate the gathering of audio encounter information 106.

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may: obtain 306encounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) from a medical professional(e.g., encounter participant 226); obtain 308 encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) from a patient (e.g., encounter participant 228);and/or obtain 310 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) from a thirdparty (e.g., encounter participant 230). Further and when obtaining 300encounter information (e.g., machine vision encounter information 102and/or audio encounter information 106), automated clinicaldocumentation process 10 may obtain 300 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) from previous (related or unrelated) patientencounters. For example, if the current patient encounter is actuallythe third visit that the patient is making concerning e.g., shortness ofbreath, the encounter information from the previous two visits (i.e.,the previous two patient encounters) may be highly-related and may beobtained 300 by automated clinical documentation process 10.

As will be discussed below in greater detail, when automated clinicaldocumentation process 10 obtains 300 the encounter information,automated clinical documentation process 10 may utilize 312 a virtualassistant (e.g., virtual assistant 238) to prompt the patient (e.g.,encounter participant 228) to provide at least a portion of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) during a pre-visit portion(e.g., a patient intake portion) of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office).

Further and as will be discussed below in greater detail, when automatedclinical documentation process 10 obtains 300 encounter information,automated clinical documentation process 10 may utilize 314 a virtualassistant (e.g., virtual assistant 238) to prompt the patient (e.g.,encounter participant 228) to provide at least a portion of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) during a post-visit portion(e.g., a patient follow-up portion) of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office).

Invention #2

As discussed above, when automated clinical documentation process 10obtains 300 encounter information, automated clinical documentationprocess 10 may utilize 312 a virtual assistant (e.g., virtual assistant238) to prompt the patient (e.g., encounter participant 228) to provideat least a portion of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) duringa pre-visit portion (e.g., a patient intake portion) of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice).

As will be discussed below in greater detail, virtual assistant 238 maybe configured to aid medical professionals (e.g., doctors, nurses,physician's assistants, lab technicians, physical therapists, scribes(e.g., a transcriptionist) and/or staff members involved in the patientencounter) with the gathering of encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during various portions of the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office).

Accordingly and referring also to FIG. 5, automated clinicaldocumentation process 10 may be configured to prompt 350 a patient(e.g., encounter participant 228) to provide encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) via a virtual assistant (e.g., virtual assistant 238)during a pre-visit portion of a patient encounter (e.g., encounterparticipant 228 visiting the doctor's office).

For example and upon arriving for the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office), the patient (e.g.,encounter participant 228) may be directed to a “check-in” area withinthe monitored space (e.g., monitored space 130) of the clinicalenvironment. An example of this “check-in” area may include a booth intowhich the patient (e.g., encounter participant 228) enters. Uponentering this “check-in” area, the pre-visit portion (e.g., the patientintake portion) of the patient encounter (e.g., encounter participant228 visiting the doctor's office) may begin.

When prompting 350 the patient (e.g., encounter participant 228) toprovide encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) via the virtualassistant (e.g., virtual assistant 238), automated clinicaldocumentation process 10 may audibly prompt 352 the patient (e.g.,encounter participant 228) to provide encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) via the virtual assistant (e.g., virtual assistant238). For example, virtual assistant 238 may provide (via audiorendering device 116) a cordial greeting to the patient (e.g., encounterparticipant 228) and ask them if they are checking in for a visit. Ifthe patient (e.g., encounter participant 228) responds affirmatively,virtual assistant 238 may audibly prompt 352 the patient (e.g.,encounter participant 228) to provide encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), examples of which may include but are not limited to:patient background information; patient current-prescriptioninformation; patient insurance information; and patient symptominformation.

Therefore, virtual assistant 238 may ask the patient (e.g., encounterparticipant 228) to provide various pieces of identifying information,examples of which may include but are not limited to: patient name,patient social security number, and patient date of birth, Dependingupon the manner in which automated clinical documentation process 10 isconfigured, machine vision encounter information 102 may be utilized tofurther enhance/expedite the check-in process. For example and viamachine vision encounter information 102, facial recognitionfunctionality may be utilized to positively identify the patient (e.g.,encounter participant 228). Additionally and via machine visionencounter information 102, visual speech recognition (via visual lipreading functionality) may be utilized by automated clinicaldocumentation process 10 to further effectuate the gathering of audioencounter information 106. Virtual assistant 238 may ask the patient(e.g., encounter participant 228) to provide additional pieces ofinformation, examples of which may include but are not limited topatient current-prescription information; patient insurance information;and patient symptom information.

While the pre-visit portion of the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office) is described above asbeing the point of the patient encounter (e.g., encounter participant228 visiting the doctor's office) where the patient (e.g., encounterparticipant 228) is entering the monitored space (e.g., monitored space130) in a clinical environment, this is for illustrative purposes onlyand is not intended to be a limitation of this disclosure, as otherconfigurations are possible and are considered to be within the scope ofthis disclosure. For example, this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor's office)may include: automated wellness phone calls, automated wellness textmessages, and automated wellness video conferences initiated by virtualassistant 238 and directed toward the patient (e.g., encounterparticipant 228) or a third party; and/or phone calls, text messages,and video conferences initiated by the patient (e.g., encounterparticipant 228) or a third party and automatically processed by virtualassistant 238.

During this pre-visit portion (e.g., the patient intake portion) of thepatient encounter (e.g., encounter participant 228 visiting the doctor'soffice), automated clinical documentation process 10 may obtain 354encounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) from the patient (e.g.,encounter participant 228) in response to the prompting by the virtualassistant (e.g., virtual assistant 238). When obtaining 354 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) from the patient (e.g.,encounter participant 228), automated clinical documentation process 10may audibly obtain 356 the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) fromthe patient (e.g., encounter participant 228), thus allowing encounterparticipant 228 to simply verbalize their answers, wherein thisinformation (e.g., audio encounter information 106) may be received viaaudio input device 30.

Automated clinical documentation process 10 may then process 302 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to generate an encountertranscript (e.g., encounter transcript 234), wherein at least a portionof the encounter transcript (e.g., encounter transcript 234) may beprocessed 304 to populate at least a portion of a medical record (e.g.,medical record 236) associated with the patient encounter (e.g., a visitto a doctor's office).

Invention #3

As discussed above, when automated clinical documentation process 10obtains 300 encounter information, automated clinical documentationprocess 10 may utilize 314 a virtual assistant (e.g., virtual assistant238) to prompt the patient (e.g., encounter participant 228) to provideat least a portion of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) duringa post-visit portion (e.g., a patient follow-up portion) of the patientencounter (e.g., encounter participant 228 visiting the doctor's office.

As discussed above and as will be discussed below in greater detail,virtual assistant 238 may be configured to aid medical professionals(e.g., doctors, nurses, physician's assistants, lab technicians,physical therapists, scribes (e.g., a transcriptionist) and/or staffmembers involved in the patient encounter) with the gathering ofencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) during various portions of thepatient encounter (e.g., encounter participant 228 visiting the doctor'soffice).

Accordingly and referring also to FIG. 6, automated clinicaldocumentation process 10 may be configured to prompt 400 a patient(e.g., encounter participant 228) to provide encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) via a virtual assistant (e.g., virtual assistant 238)during a post-visit portion of a patient encounter (e.g., encounterparticipant 228 visiting the doctor's office).

For example and upon completing the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office), the patient (e.g.,encounter participant 228) may be directed to a “check-out” area withinthe monitored space (e.g., monitored space 130) of the clinicalenvironment. An example of this “check-out” area may include a boothinto which the patient (e.g., encounter participant 228) enters. Uponentering this “check-out” area, the post-visit portion (e.g., thepatient follow-up portion) of the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office) may begin.

When prompting 400 the patient (e.g., encounter participant 228) toprovide encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) via the virtualassistant (e.g., virtual assistant 238), automated clinicaldocumentation process 10 may audibly prompt 402 the patient (e.g.,encounter participant 228) to provide encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) via the virtual assistant (e.g., virtual assistant238). For example, virtual assistant 238 may provide (via audiorendering device 116) a cordial greeting to the patient (e.g., encounterparticipant 228) and ask them if they are checking out. If the patient(e.g., encounter participant 228) responds affirmatively, virtualassistant 238 may audibly prompt 402 the patient (e.g., encounterparticipant 228) to provide encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106),examples of which may include but are not limited to: patient statusinformation; patient medication information; and patient follow-upinformation.

Therefore, virtual assistant 238 may ask the patient (e.g., encounterparticipant 228) to provide various pieces of identifying information,examples of which may include but are not limited to: patient statusinformation; patient medication information; and patient follow-upinformation, Depending upon the manner in which automated clinicaldocumentation process 10 is configured, machine vision encounterinformation 102 may be utilized to further enhance/expedite thecheck-out process. For example and via machine vision encounterinformation 102, facial recognition functionality may be utilized topositively identify the patient (e.g., encounter participant 228).Additionally and via machine vision encounter information 102, visualspeech recognition (via visual lip reading functionality) may beutilized by automated clinical documentation process 10 to furthereffectuate the gathering of audio encounter information 106.

While the post-visit portion of the patient encounter (e.g., encounterparticipant 228 visiting the doctor's office) is described above asbeing the point of the patient encounter (e.g., encounter participant228 visiting the doctor's office) where the patient (e.g., encounterparticipant 228) is leaving the monitored space (e.g., monitored space130) in a clinical environment, this is for illustrative purposes onlyand is not intended to be a limitation of this disclosure, as otherconfigurations are possible and are considered to be within the scope ofthis disclosure. For example, this post-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor's office)may include: automated wellness phone calls, automated wellness textmessages, and automated wellness video conferences initiated by virtualassistant 238 and directed toward the patient (e.g., encounterparticipant 228) or a third party; and/or phone calls, text messages,and video conferences initiated by the patient (e.g., encounterparticipant 228) or a third party and automatically processed by virtualassistant 238.

During this post-visit portion (e.g., the patient follow-up portion) ofthe patient encounter (e.g., encounter participant 228 visiting thedoctor's office), automated clinical documentation process 10 may obtain404 encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) from the patient (e.g.,encounter participant 228) in response to the prompting by the virtualassistant (e.g., virtual assistant 238). When obtaining 404 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from the patient (e.g., encounter participant228), automated clinical documentation process 10 may audibly obtain 406encounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) from the patient (e.g.,encounter participant 228), thus allowing encounter participant 228 tosimply verbalize their answers, wherein this information (e.g., audioencounter information 106) may be received via audio input device 30.

Automated clinical documentation process 10 may then process 302 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to generate an encountertranscript (e.g., encounter transcript 234), wherein at least a portionof the encounter transcript (e.g., encounter transcript 234) may beprocessed 304 to populate at least a portion of a medical record (e.g.,medical record 236) associated with the patient encounter (e.g., a visitto a doctor's office).

Invention #4

Automated clinical documentation process 10 may be configured to monitorthe interaction between the patient (e.g., encounter participant 228)and the medical professionals (e.g., a doctor, a nurse, a physician'sassistant, a lab technician, a physical therapist and/or a staff memberinvolved in the patient encounter) during the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) to determine ifany potential medical situations are missed.

Accordingly and referring also to FIG. 7, automated clinicaldocumentation process 10 may obtain 300 encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) of a patient encounter (e.g., a visit to a doctor'soffice).

As discussed above, since machine vision system 100 and audio recordingsystem 104 may be positioned throughout monitored space 130, all of theinteractions between medical professionals (e.g., encounter participant226), patients (e.g., encounter participant 228) and third parties(e.g., encounter participant 230) that occur during the patientencounter (e.g., encounter participant 228 visiting the doctor's office)within the monitored space (e.g., monitored space 130) of the clinicalenvironment (e.g., the doctor's office) may bemonitored/recorded/processed. Accordingly, a patient “check-in” areawithin monitored space 130 may be monitored to obtain encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) during this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice). Additionally and as discussed above, this pre-visit encounterinformation may be obtained via e.g., virtual assistant 238 before thepatient (e.g., encounter participant 228) has entered monitored space130. Further, various rooms within monitored space 130 may be monitoredto obtain encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during thesevarious portions of the patient encounter (e.g., while meeting with thedoctor, while vital signs and statistics are obtained, and while imagingis performed). Further, a patient “check-out” area within monitoredspace 130 may be monitored to obtain encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) during this post-visit portion of the patient encounter(e.g., encounter participant 228 visiting the doctor's office).Additionally and as discussed above, this post-visit encounterinformation may be obtained via e.g., virtual assistant 238 after thepatient (e.g., encounter participant 228) has left monitored space 130.Further and via machine vision encounter information 102, visual speechrecognition (via visual lip reading functionality) may be utilized byautomated clinical documentation process 10 to further effectuate thegathering of audio encounter information 106.

Accordingly, a complete recording of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) may begenerated, wherein this encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) may beprocessed 302 to generate an encounter transcript (e.g., encountertranscript 234) and at least a portion of this encounter transcript(e.g., encounter transcript 234) may be processed 304 to populate atleast a portion of a medical record (e.g., medical record 236)associated with the patient encounter (e.g., the visit to the doctor'soffice).

Automated clinical documentation process 10 may process 450 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to determine if the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) is indicative of a potential medicalsituation, wherein examples of these potential medical situations mayinclude but are not limited to one or more of: a potential medicalcondition; a potential medication issue; a potential home healthcareissue; and a potential follow-up issue.

As discussed above, ACD compute system 12 may be configured to accessone or more datasources (e.g., datasources 118), wherein examples ofdatasources 118 may include a medical conditions symptoms datasource(e.g., that defines the symptoms for various diseases and medicalconditions), a prescriptions compatibility datasource (e.g., thatdefines groups of prescriptions that are substitutable for (orcompatible with) each other), a medical insurance coverage datasource(e.g., that defines what prescriptions are covered by various medicalinsurance providers), and a home healthcare datasource (e.g., thatdefines best practices concerning when home healthcare is advisable).Accordingly, automated clinical documentation process 10 may process 450the data included within the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) tocompare this data to data defined within the datasources (e.g.,datasources 118) to determine if the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) is indicative of a potential medical situation.

For example, assume that the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106)indicates that the patient (e.g., encounter participant 228) mentionedduring the patient encounter (e.g., encounter participant 228 visitingthe doctor's office) that their wound was healing slowly. Can that beindicative of high blood sugar? Further suppose that the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) indicates that the patient (e.g., encounterparticipant 228) is quite elderly, lives alone and now needs to takeinjectable medication every day for the next week. Should home healthcare be arranged to medicate this patient? Additionally, suppose theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) indicates that the doctor (e.g.,encounter participant 226) prescribed medication X. Does the patient'smedical insurance cover Medication X (or do they only cover MedicationY)?

If a potential medical situation is identified, automated clinicaldocumentation process 10 may initiate 452 an inquiry concerning thepotential medical situation. When initiating 452 an inquiry concerningthe potential medical situation, automated clinical documentationprocess 10 may provide 454 a notification (e.g., as visual information110 and/or audio information 114) to a medical professional (e.g., adoctor, a nurse, a physician's assistant, a lab technician, a physicaltherapist and/or a staff member involved in the patient encounter)concerning the potential medical situation. Example of such an inquirymay be asking one or more questions, such as “Does this patient havediabetes?”, “Should we arrange home healthcare for this patient?” of“Would you like to substitute Medication Y for Medication X?”

When providing 454 a notification (e.g., as visual information 110and/or audio information 114) to a medical professional (e.g., a doctor,a nurse, a physician's assistant, a lab technician, a physical therapistand/or a staff member involved in the patient encounter) concerning thepotential medical situation, automated clinical documentation process 10may provide 456 a private text-based notification (e.g., as visualinformation 110) to the medical professional (e.g., a doctor, a nurse, aphysician's assistant, a lab technician, a physical therapist and/or astaff member involved in the patient encounter) concerning the potentialmedical situation. This private text-based notification (e.g., as visualinformation 110) may be provided to the medical professional on e.g., aprivate display device, examples of which may include but are notlimited to a smart phone, a table computer, a notebook computer, or adesktop computer.

When providing 454 a notification (e.g., as visual information 110and/or audio information 114) to a medical professional (e.g., a doctor,a nurse, a physician's assistant, a lab technician, a physical therapistand/or a staff member involved in the patient encounter) concerning thepotential medical situation, automated clinical documentation process 10may provide 458 a private audio-based notification (e.g., as audioinformation 114) to the medical professional (e.g., a doctor, a nurse, aphysician's assistant, a lab technician, a physical therapist and/or astaff member involved in the patient encounter) concerning the potentialmedical situation. This private audio-based notification (e.g., as audioinformation 114) may be provided to the medical professional on e.g., aprivate audio device, examples of which may include but are not limitedto a smart phone, or an earbud.

Alternatively, when initiating 452 an inquiry concerning the potentialmedical situation, automated clinical documentation process 10 mayinquire 460 about the potential medical situation via a virtualassistant (e.g., virtual assistant 238), wherein inquiring 460 about thepotential medical situation via a virtual assistant (e.g., virtualassistant 238) may include verbally inquiring 462 about the potentialmedical situation via a virtual assistant (e.g., virtual assistant 238).For example and in such a configuration, when initiating 452 theinquiry, automated clinical documentation process 10 may inquire 460about the potential medical situation by having virtual assistant 238verbally (and publically) inquire 462 by asking one or more questions,such as “Does this patient have diabetes?”, “Should we arrange homehealthcare for this patient?” of “Would you like to substituteMedication Y for Medication X?”

Invention #5

Automated clinical documentation process 10 may be configured tosimultaneously render content to multiple output devices at varyinglevels of detail, as it may be desirable to not broadcast certain“sensitive” content within the examination room.

Accordingly and referring also to FIG. 8, automated clinicaldocumentation process 10 may obtain 300 encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) of a patient encounter (e.g., a visit to a doctor'soffice). Assume that during the course of the patient encounter (e.g., avisit to a doctor's office), a medical professional (e.g., a doctor, anurse, a physician's assistant, a lab technician, a physical therapistand/or a staff member involved in the patient encounter) wishes torender a piece of content for viewing. For example and as discussedabove, during the various portions of the patient encounter (e.g., avisit to a doctor's office), various procedures may occur, an example ofwhich includes but is not limited to an imaging procedure. Accordingly,assume that the medical professional would like to display one or moreimages for viewing within the monitored space (e.g., monitored space130). Accordingly, automated clinical documentation process 10 may beconfigured to receive 500 a request to render clinical content (e.g., anx-ray image) during a patient encounter (e.g., a visit to a doctor'soffice). This rendering request may be in the form of a verbal request(e.g., audio encounter information 106) that is spoken by e.g., amedical professional or a computer-based request that is initiated bythe medical processional via ACD client electronic devices 28, 32.

Upon receiving 500 such a request, automated clinical documentationprocess 10 may determine 502 if the clinical content (e.g., the x-rayimage) includes sensitive content. Examples of such sensitive contentmay include but are not limited to one or more of: sensitive image-basedcontent; sensitive text-based content; sensitive prognosis-basedcontent; sensitive diagnosis-based content; and sensitivecomplication-based content.

If the clinical content includes sensitive content, automated clinicaldocumentation process 10 may render 504: a complete version of theclinical content (e.g., an x-ray image) on a first device (wherein thecomplete version of the clinical content includes the sensitive content)and a limited version of the clinical content (e.g., an x-ray image) ona second device (wherein the limited version of the clinical contentexcludes the sensitive content).

For this example, the first device may be a private device availableonly to one or more medical professionals of the patient encounter(e.g., a visit to a doctor's office). Examples of such private devicesmay include but are not limited to a visual private device and anaudible private device. For this example, the second device may be apublic device available to all encounter participants of the patientencounter (e.g., a visit to a doctor's office). Examples of such publicdevices may include but are not limited to a visual public device.

For example, assume that upon receiving 500 a request to render theclinical content (e.g., the x-ray image), automated clinicaldocumentation process 10 determines 502 that the clinical content (e.g.,the x-ray image) does include sensitive content (e.g., a strange mass).Accordingly, automated clinical documentation process 10 may render 504a complete version of the x-ray image (that includes annotationshighlighting the strange mass) on a first device e.g., a private tabletcomputer only accessible to the medical professional (e.g., a doctor, anurse, a physician's assistant, a lab technician, a physical therapistand/or a staff member involved in the patient encounter). Further,automated clinical documentation process 10 may render 504 a limitedversion of the x-ray image (that excludes annotations highlighting thestrange mass) on a second device (e.g., a wall-mounted television withinmonitored space 130).

As another example, automated clinical documentation process 10 mayrender 504 a complete version of the patient's symptoms on a seconddevice (e.g., a wall-mounted television within monitored space 130),wherein some of these symptoms may be indicative of diabetes.Accordingly, automated clinical documentation process 10 may render 504a private message (e.g., as a text-based message or an audio-basedmessage) on a first device (e.g., a private tablet computer accessibleto the doctor or a private earbud worn by the doctor) indicating thatsome of these symptoms may be indicative of diabetes and, therefore, thedoctor may wish to order an A1C test.

Invention #7

Automated clinical documentation process 10 may be configured to processthe encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) to generate encountertranscript 234 that may be automatically formatted and punctuated.

Accordingly and referring also to FIG. 9, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

When obtaining 300 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106), automatedclinical documentation process 10 may obtain 306 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from a medical professional; obtain 308 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) from a patient; and/or obtain310 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) from a thirdparty. Further and when obtaining 300 encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), automated clinical documentation process 10 may obtain300 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) from previous(related or unrelated) patient encounters. For example, if the currentpatient encounter is actually the third visit that the patient is makingconcerning e.g., shortness of breath, the encounter information from theprevious two visits (i.e., the previous two patient encounters) may behighly-related and may be obtained 300 by automated clinicaldocumentation process 10.

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Automated clinical documentation process 10 may process 550 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to: associate a first portion ofthe encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) with a first encounterparticipant, and associate at least a second portion of the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) with at least a second encounter participant.

As discussed above, modular ACD system 54 may be configured to form oneor more audio recording beams (e.g., audio recording beams 220, 222,224) via the discrete audio acquisition devices (e.g., discrete audioacquisition devices 202, 204, 206, 208, 210, 212, 214, 216, 218)included within audio recording system 104, wherein modular ACD system54 may be further configured to steer the one or more audio recordingbeams (e.g., audio recording beams 220, 222, 224) toward one or moreencounter participants (e.g., encounter participants 226, 228, 230) ofthe above-described patient encounter.

Accordingly and continuing with the above-stated example, modular ACDsystem 54 may steer audio recording beam 220 toward encounterparticipant 226, may steer audio recording beam 222 toward encounterparticipant 228, and may steer audio recording beam 224 toward encounterparticipant 230. Accordingly and due to the directionality of audiorecording beams 220, 222, 224, audio encounter information 106 mayinclude three components, namely audio encounter information 106A (whichis obtained via audio recording beam 220), audio encounter information106B (which is obtained via audio recording beam 222) and audioencounter information 106C (which is obtained via audio recording beam220).

Further and as discussed above, ACD compute system 12 may be configuredto access one or more datasources 118 (e.g., plurality of individualdatasources 120, 122, 124, 126, 128), examples of which may include butare not limited to one or more of a user profile datasource, a voiceprint datasource, a voice characteristics datasource (e.g., for adaptingthe automated speech recognition models), a face print datasource, ahumanoid shape datasource, an utterance identifier datasource, awearable token identifier datasource, an interaction identifierdatasource, a medical conditions symptoms datasource, a prescriptionscompatibility datasource, a medical insurance coverage datasource, and ahome healthcare datasource.

Accordingly, automated clinical documentation process 10 may process 550the encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) to: associate a firstportion (e.g., encounter information 106A) of the encounter information(e.g., audio encounter information 106) with a first encounterparticipant (e.g., encounter participant 226), and associate at least asecond portion (e.g., encounter information 106B, 106C) of the encounterinformation (e.g., audio encounter information 106) with at least asecond encounter participant (e.g., encounter participants 228, 230;respectively).

Further and when processing 550 the encounter information (e.g., audioencounter information 106A, 106B, 106C), automated clinicaldocumentation process 10 may compare each of audio encounter information106A, 106B, 106C to the voice prints defined within the above-referencedvoice print datasource so that the identity of encounter participants226, 228, 230 (respectively) may be determined. Accordingly, if thevoice print datasource includes a voice print that corresponds to one ormore of the voice of encounter participant 226 (as heard within audioencounter information 106A), the voice of encounter participant 228 (asheard within audio encounter information 106B) or the voice of encounterparticipant 230 (as heard within audio encounter information 106C), theidentity of one or more of encounter participants 226, 228, 230 may bedefined. And in the event that a voice heard within one or more of audioencounter information 106A, audio encounter information 106B or audioencounter information 106C is unidentifiable, that one or moreparticular encounter participant may be defined as “UnknownParticipant”.

Once the voices of encounter participants 226, 228, 230 are processed550, automated clinical documentation process 10 may generate 302 anencounter transcript (e.g., encounter transcript 234) based, at least inpart, upon the first portion of the encounter information (e.g., audioencounter information 106A) and the at least a second portion of theencounter information (e.g., audio encounter information 106B. 106C).

Invention #8

Automated clinical documentation process 10 may be configured toautomatically define roles for the encounter participants (e.g.,encounter participants 226, 228, 230) in the patient encounter (e.g., avisit to a doctor's office).

Accordingly and referring also to FIG. 10, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

Automated clinical documentation process 10 may then process 600 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to associate a first portion ofthe encounter information with a first encounter participant (e.g.,encounter participant 226) and assign 602 a first role to the firstencounter participant (e.g., encounter participant 226).

When processing 600 the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) toassociate the first portion of the encounter information with the firstencounter participant (e.g., encounter participant 226), automatedclinical documentation process 10 may process 604 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) to associate a first portion of the audioencounter information (e.g., audio encounter information 106A) with thefirst encounter participant (e.g., encounter participant 226).

Specifically and when processing 604 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) to associate the first portion of the audio encounterinformation (e.g., audio encounter information 106A) with the firstencounter participant (e.g., encounter participant 226), automatedclinical documentation process 10 may compare 606 one or more voiceprints (defined within voice print datasource) to one or more voicesdefined within the first portion of the audio encounter information(e.g., audio encounter information 106A); and may compare 608 one ormore utterance identifiers (defined within utterance datasource) to oneor more utterances defined within the first portion of the audioencounter information (e.g., audio encounter information 106A); whereincomparisons 606, 608 may allow automated clinical documentation process10 to assign 602 a first role to the first encounter participant (e.g.,encounter participant 226). For example, if the identity of encounterparticipant 226 can be defined via voice prints, a role for encounterparticipant 226 may be assigned 602 if that identity defined isassociated with a role (e.g., the identity defined for encounterparticipant 226 is Doctor Susan Jones). Further, if an utterance made byencounter participant 226 is “I am Doctor Susan Jones”, this utterancemay allow a role for encounter participant 226 to be assigned 602.

When processing 600 the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) toassociate the first portion of the encounter information with the firstencounter participant (e.g., encounter participant 226), automatedclinical documentation process 10 may process 610 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) to associate a first portion of the machinevision encounter information (e.g., machine vision encounter information102A) with the first encounter participant (e.g., encounter participant226).

Specifically and when processing 610 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) to associate the first portion of the machine visionencounter information (e.g., machine vision encounter information 102A)with the first encounter participant (e.g., encounter participant 226),automated clinical documentation process 10 may compare 612 one or moreface prints (defined within face print datasource) to one or more facesdefined within the first portion of the machine vision encounterinformation (e.g., machine vision encounter information 102A); compare614 one or more wearable token identifiers (defined within wearabletoken identifier datasource) to one or more wearable tokens definedwithin the first portion of the machine vision encounter information(e.g., machine vision encounter information 102A); and compare 616 oneor more interaction identifiers (defined within interaction identifierdatasource) to one or more humanoid interactions defined within thefirst portion of the machine vision encounter information (e.g., machinevision encounter information 102A); wherein comparisons 612, 614, 616may allow automated clinical documentation process 10 to assign 602 afirst role to the first encounter participant (e.g., encounterparticipant 226). For example, if the identity of encounter participant226 can be defined via face prints, a role for encounter participant 226may be assigned 602 if that identity defined is associated with a role(e.g., the identity defined for encounter participant 226 is DoctorSusan Jones). Further, if a wearable token worn by encounter participant226 can be identified as a wearable token assigned to Doctor SusanJones, a role for encounter participant 226 may be assigned 602.Additionally, if an interaction made by encounter participant 226corresponds to the type of interaction that is made by a doctor, theexistence of this interaction may allow a role for encounter participant226 to be assigned 602.

Examples of such wearable tokens may include but are not limited towearable devices that may be worn by the medical professionals when theyare within monitored space 130 (or after they leave monitored space130). For example, these wearable tokens may be worn by medicalprofessionals when e.g., they are moving between monitored rooms withinmonitored space 130, travelling to and/or from monitored space 130,and/or outside of monitored space 130 (e.g., at home).

Additionally, automated clinical documentation process 10 may process618 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to associate atleast a second portion of the encounter information with at least asecond encounter participant; and may assign 620 at least a second roleto the at least a second encounter participant.

Specifically, automated clinical documentation process 10 may process618 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to associate atleast a second portion of the encounter information with at least asecond encounter participant. For example, automated clinicaldocumentation process 10 may process 618 the encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) to associate audio encounter information 106B andmachine vision encounter information 102B with encounter participant 228and may associate audio encounter information 106C and machine visionencounter information 102C with encounter participant 230.

Further, automated clinical documentation process 10 may assign 620 atleast a second role to the at least a second encounter participant. Forexample, automated clinical documentation process 10 may assign 620 arole to encounter participants 228, 230.

Invention #9

Automated clinical documentation process 10 may be configured to monitormultiple encounter participants (e.g., encounter participants 226, 228,230) within a patient encounter (e.g., a visit to a doctor's office),wherein some of these encounter participants may be identifiable via aunique voice print profile, while others may be unidentifiable due to alack of a unique voice print/profile.

Accordingly and referring also to FIG. 11, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

Automated clinical documentation process 10 may process 650 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to: associate 652 at least afirst portion of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) withat least one known encounter participant, and associate 654 at least asecond portion of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) withat least one unknown encounter participant.

As discussed above, modular ACD system 54 may be configured to form oneor more audio recording beams (e.g., audio recording beams 220, 222,224) via the discrete audio acquisition devices (e.g., discrete audioacquisition devices 202, 204, 206, 208, 210, 212, 214, 216, 218)included within audio recording system 104, wherein modular ACD system54 may be further configured to steer the one or more audio recordingbeams (e.g., audio recording beams 220, 222, 224) toward one or moreencounter participants (e.g., encounter participants 226, 228, 230) ofthe above-described patient encounter.

Accordingly and continuing with the above-stated example, modular ACDsystem 54 may steer audio recording beam 220 toward encounterparticipant 226, may steer audio recording beam 222 toward encounterparticipant 228, and may steer audio recording beam 224 toward encounterparticipant 230. Accordingly and due to the directionality of audiorecording beams 220, 222, 224, audio encounter information 106 mayinclude three components, namely audio encounter information 106A (whichis obtained via audio recording beam 220), audio encounter information106B (which is obtained via audio recording beam 222) and audioencounter information 106C (which is obtained via audio recording beam220).

Further and as discussed above, ACD compute system 12 may be configuredto access one or more datasources 118 (e.g., plurality of individualdatasources 120, 122, 124, 126, 128), examples of which may include butare not limited to one or more of a user profile datasource, a voiceprint datasource, a voice characteristics datasource (e.g., for adaptingthe automated speech recognition models), a face print datasource, ahumanoid shape datasource, an utterance identifier datasource, awearable token identifier datasource, an interaction identifierdatasource, a medical conditions symptoms datasource, a prescriptionscompatibility datasource, a medical insurance coverage datasource, and ahome healthcare datasource.

When associating 652 at least a first portion of the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) with at least one known encounterparticipant, automated clinical documentation process 10 may compare 656the data included within the user profile (defined within the userprofile datasource) to the at least a first portion of the audioencounter information. The data included within the user profile mayinclude voice-related data (e.g., a voice print that is defined locallywithin the user profile or remotely within the voice print datasource),language use patterns, user accent identifiers, user-defined macros, anduser-defined shortcuts, for example.

Specifically and when attempting to associate 652 at least a firstportion of the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) with at leastone known encounter participant, automated clinical documentationprocess 10 may compare 656 one or more voice prints (defined within thevoice print datasource) to one or more voices defined within the firstportion of the audio encounter information (e.g., audio encounterinformation 106A); may compare 656 one or more voice prints (definedwithin the voice print datasource) to one or more voices defined withinthe second portion of the audio encounter information (e.g., audioencounter information 106B); and may compare 656 one or more voiceprints (defined within the voice print datasource) to one or more voicesdefined within the third portion of the audio encounter information(e.g., audio encounter information 106C).

As discussed above and for this example, assume: that encounterparticipant 226 is a medical professional that has a voiceprint/profile; that encounter participant 228 is a long-term patientthat has a voice print/profile; and that encounter participant 230 is athird party (the acquaintance of encounter participant 228) and,therefore, does not have a voice print/profile. Accordingly and for thisexample: assume that automated clinical documentation process 10 will besuccessful and identify encounter participant 226 when comparing 656audio encounter information 106A to the various voice prints/profilesincluded within voice print datasource; assume that automated clinicaldocumentation process 10 will be successful and identify encounterparticipant 228 when comparing 656 audio encounter information 106B tothe various voice prints/profiles included within voice printdatasource; and assume that automated clinical documentation process 10will be unsuccessful and not identify encounter participant 230 whencomparing 656 audio encounter information 106C to the various voiceprints/profiles included within voice print datasource.

Accordingly and when processing 650 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), automated clinical documentation process 10 mayassociate 652 audio encounter information 106A with the voiceprint/profile of Doctor Susan Jones and may identify encounterparticipant 226 as “Doctor Susan Jones”. Automated clinicaldocumentation process 10 may further associate 654 audio encounterinformation 106B with the voice print/profile of Patient Paul Smith andmay identify encounter participant 228 as “Patient Paul Smith”. Further,automated clinical documentation process 10 may not be able to associate654 audio encounter information 106C with any voice prints/profiles andmay identify encounter participant 230 as “Unknown Participant”.

Automated clinical documentation process 10 may generate 658 anencounter transcript (e.g., encounter transcript 234) based, at least inpart, upon the at least a first portion of the encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) and the at least a second portion of the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106). Accordingly, automated clinicaldocumentation process 10 may generate 658 an encounter transcript (e.g.,encounter transcript 234) that identifies the verbal comments andutterances made by “Doctor Susan Jones”, “Patient Paul Smith” and“Unknown Participant”.

Invention #10

Automated clinical documentation process 10 may be configured to usesemantic frames as an intermediary step between encounter transcript 234and medical record 236, wherein these semantic frames may define anabstract meaning of a portion of encounter transcript and this abstractmeaning may be used when populating medical record 236.

Accordingly and referring also to FIG. 12, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

As discussed above, since machine vision system 100 and audio recordingsystem 104 may be positioned throughout monitored space 130, all of theinteractions between medical professionals (e.g., encounter participant226), patients (e.g., encounter participant 228) and third parties(e.g., encounter participant 230) that occur during the patientencounter (e.g., encounter participant 228 visiting the doctor's office)within the monitored space (e.g., monitored space 130) of the clinicalenvironment (e.g., the doctor's office) may bemonitored/recorded/processed. Accordingly, a patient “check-in” areawithin monitored space 130 may be monitored to obtain encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) during this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice). Additionally and as discussed above, this pre-visit encounterinformation may be obtained via e.g., virtual assistant 238 before thepatient (e.g., encounter participant 228) has entered monitored space130. Further, various rooms within monitored space 130 may be monitoredto obtain encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during thesevarious portions of the patient encounter (e.g., while meeting with thedoctor, while vital signs and statistics are obtained, and while imagingis performed). Further, a patient “check-out” area within monitoredspace 130 may be monitored to obtain encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) during this post-visit portion of the patient encounter(e.g., encounter participant 228 visiting the doctor's office).Additionally and as discussed above, this post-visit encounterinformation may be obtained via e.g., virtual assistant 238 after thepatient (e.g., encounter participant 228) has left monitored space 130.Further and via machine vision encounter information 102, visual speechrecognition (via visual lip reading functionality) may be utilized byautomated clinical documentation process 10 to further effectuate thegathering of audio encounter information 106.

Accordingly, a complete recording of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) may begenerated, wherein this encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) may beprocessed 302 to generate an encounter transcript (e.g., encountertranscript 234).

As is known in the art, a semantic frame (e.g., semantic frame 240) maybe a collection of facts that specify “characteristic features,attributes, and functions of a denotatum, and its characteristicinteractions with things necessarily or typically associated with it.”Accordingly, a semantic frame (e.g., semantic frame 240) may be definedas a coherent structure of related concepts that are related such thatwithout knowledge of all of the related concepts, one does not havecomplete knowledge of any of the related concepts. Further, a semanticframe (e.g., semantic frame 240) may be based on recurring experiences,wherein e.g., a commercial transaction semantic frame may be based uponrecurring experiences in the commercial transactions space. Accordingly,a semantic frame (e.g., semantic frame 240) may define an abstractmeaning of a portion of an encounter transcript.

Accordingly, automated clinical documentation process 10 may generate700 at least one semantic frame (e.g., semantic frame 240) based, atleast in part, upon at least one portion of encounter transcript 234,wherein the at least one semantic frame (e.g., semantic frame 240) maydefine an abstract meaning for the at least one portion of encountertranscript 234.

Referring also to FIGS. 13A-13E, there are shown various illustrativeexamples concerning the manner in which automated clinical documentationprocess 10 may generate 700 at least one semantic frame (e.g., semanticframe 240) based, at least in part, upon at least one portion ofencounter transcript 234, Specifically and as shown in these figures,various discrete portions of encounter transcript 234 may be mapped ontothe various semantic frames.

Automated clinical documentation process 10 may then process 702 the atleast one semantic frame (e.g., semantic frame 240) to populate at leasta portion of a medical record (e.g., medical record 236) associated withthe patient encounter (e.g., a visit to a doctor's office).

Referring also to FIGS. 14A-14B, there are shown various illustrativeexamples concerning the manner in which automated clinical documentationprocess 10 may process 702 the at least one semantic frame (e.g.,semantic frame 240) to populate at least a portion of a medical record(e.g., medical record 236). Specifically and as shown in these figures,various discrete pieces of data defined within the various semanticframes (e.g., semantic frame 240) may be used to populate medical record236.

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Invention #12

Automated clinical documentation process 10 may be configured to trackthe movement and/or interaction of humanoid shapes within the monitoredspace (e.g., monitored space 130) during the patient encounter (e.g., avisit to a doctor's office) so that e.g., the automated clinicaldocumentation process 10 knows when encounter participants (e.g., one ormore of encounter participants 226, 228, 230) enter, exit or cross pathswithin monitored space 130.

Accordingly and referring also to FIG. 15, automated clinicaldocumentation process 10 may process 750 the machine vision encounterinformation (e.g., machine vision encounter information 102) to identifyone or more humanoid shapes. As discussed above, examples of machinevision system 100 generally (and ACD client electronic device 34specifically) may include but are not limited to one or more of an RGBimaging system, an infrared imaging system, an ultraviolet imagingsystem, a laser imaging system, a SONAR imaging system, a RADAR imagingsystem, and a thermal imaging system).

When ACD client electronic device 34 includes a visible light imagingsystem (e.g., an RGB imaging system), ACD client electronic device 34may be configured to monitor various objects within monitored space 130by recording motion video in the visible light spectrum of these variousobjects. When ACD client electronic device 34 includes an invisiblelight imaging systems (e.g., a laser imaging system, an infrared imagingsystem and/or an ultraviolet imaging system), ACD client electronicdevice 34 may be configured to monitor various objects within monitoredspace 130 by recording motion video in the invisible light spectrum ofthese various objects. When ACD client electronic device 34 includes anX-ray imaging system, ACD client electronic device 34 may be configuredto monitor various objects within monitored space 130 by recordingenergy in the X-ray spectrum of these various objects. When ACD clientelectronic device 34 includes a SONAR imaging system, ACD clientelectronic device 34 may be configured to monitor various objects withinmonitored space 130 by transmitting soundwaves that may be reflected offof these various objects. When ACD client electronic device 34 includesa RADAR imaging system, ACD client electronic device 34 may beconfigured to monitor various objects within monitored space 130 bytransmitting radio waves that may be reflected off of these variousobjects. When ACD client electronic device 34 includes a thermal imagingsystem, ACD client electronic device 34 may be configured to monitorvarious objects within monitored space 130 by tracking the thermalenergy of these various objects.

As discussed above, ACD compute system 12 may be configured to accessone or more datasources 118 (e.g., plurality of individual datasources120, 122, 124, 126, 128), wherein examples of which may include but arenot limited to one or more of a user profile datasource, a voice printdatasource, a voice characteristics datasource (e.g., for adapting theautomated speech recognition models), a face print datasource, ahumanoid shape datasource, a humanoid shape datasource, an utteranceidentifier datasource, a wearable token identifier datasource, aninteraction identifier datasource, a medical conditions symptomsdatasource, a prescriptions compatibility datasource, a medicalinsurance coverage datasource, and a home healthcare datasource.

Accordingly and when processing 750 the machine vision encounterinformation (e.g., machine vision encounter information 102) to identifyone or more humanoid shapes, automated clinical documentation process 10may be configured to compare the humanoid shapes defined within one ormore datasources 118 to potential humanoid shapes within the machinevision encounter information (e.g., machine vision encounter information102).

When processing 750 the machine vision encounter information (e.g.,machine vision encounter information 102) to identify one or morehumanoid shapes, automated clinical documentation process 10 may track752 the movement of the one or more humanoid shapes within the monitoredspace (e.g., monitored space 130). For example and when tracking 752 themovement of the one or more humanoid shapes within monitored space 130,automated clinical documentation process 10 may add 754 a new humanoidshape to the one or more humanoid shapes when the new humanoid shapeenters the monitored space (e.g., monitored space 130) and/or may remove756 an existing humanoid shape from the one or more humanoid shapes whenthe existing humanoid shape leaves the monitored space (e.g., monitoredspace 130).

For example, assume that a lab technician (e.g., encounter participant242) temporarily enters monitored space 130 to chat with encounterparticipant 230. Accordingly, automated clinical documentation process10 may add 754 encounter participant 242 to the one or more humanoidshapes being tracked 752 when the new humanoid shape (i.e., encounterparticipant 242) enters monitored space 130. Further, assume that thelab technician (e.g., encounter participant 242) leaves monitored space130 after chatting with encounter participant 230. Therefore, automatedclinical documentation process 10 may remove 756 encounter participant242 from the one or more humanoid shapes being tracked 752 when thehumanoid shape (i.e., encounter participant 242) leaves monitored space130.

Also and when tracking 752 the movement of the one or more humanoidshapes within monitored space 130, automated clinical documentationprocess 10 may monitor the trajectories of the various humanoid shapeswithin monitored space 130. Accordingly, assume that when leavingmonitored space 130, encounter participant 242 walks in front of (orbehind) encounter participant 226. As automated clinical documentationprocess 10 is monitoring the trajectories of (in this example) encounterparticipant 242 (who is e.g., moving from left to right) and encounterparticipant 226 (who is e.g., stationary), when encounter participant242 passes in front of (or behind) encounter participant 226, theidentities of these two humanoid shapes may not be confused by automatedclinical documentation process 10.

Automated clinical documentation process 10 may be configured to obtain300 the encounter information of the patient encounter (e.g., a visit toa doctor's office), which may include machine vision encounterinformation 102 (in the manner described above) and/or audio encounterinformation 106.

Automated clinical documentation process 10 may steer 758 one or moreaudio recording beams (e.g., audio recording beams 220, 222, 224) towardthe one or more humanoid shapes (e.g., encounter participants 226, 228,230) to capture audio encounter information (e.g., audio encounterinformation 106), wherein audio encounter information 106 may beincluded within the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106).

Specifically, automated clinical documentation process 10 (via modularACD system 54 and/or audio recording system 104) may utilize one or moreof the discrete audio acquisition devices (e.g., audio acquisitiondevices 202, 204, 206, 208, 210, 212, 214, 216, 218) to form an audiorecording beam. For example, automated clinical documentation process 10may utilize audio acquisition device 210 to form audio recording beam220, thus enabling the capturing of audio (e.g., speech) produced byencounter participant 226 (as audio acquisition device 210 is pointed to(i.e., directed toward) encounter participant 226). Additionally,automated clinical documentation process 10 may utilize audioacquisition devices 204, 206 to form audio recording beam 222, thusenabling the capturing of audio (e.g., speech) produced by encounterparticipant 228 (as audio acquisition devices 204, 206 are pointed to(i.e., directed toward) encounter participant 228). Additionally,automated clinical documentation process 10 may utilize audioacquisition devices 212, 214 to form audio recording beam 224, thusenabling the capturing of audio (e.g., speech) produced by encounterparticipant 230 (as audio acquisition devices 212, 214 are pointed to(i.e., directed toward) encounter participant 230).

Once obtained, automated clinical documentation process 10 may process302 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to generateencounter transcript 234 and may process 304 at least a portion ofencounter transcript 234 to populate at least a portion of a medicalrecord (e.g., medical record 236) associated with the patient encounter(e.g., a visit to a doctor's office).

Invention #13

Automated clinical documentation process 10 may be configured to filterout audio that does not belong within audio recording beams (e.g., audiorecording beams 220, 222, 224) using e.g., echo cancellation and/orblind source processing.

Accordingly and referring also to FIG. 16, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office), wherein the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)includes first audio encounter information obtained from a firstencounter participant and at least a second audio encounter informationobtained from at least a second encounter participant.

When obtaining 300 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) of a patientencounter (e.g., a visit to a doctor's office), automated clinicaldocumentation process 10 may steer 800 a first audio recording beamtoward the first encounter participant; and may steer 802 at least asecond audio recording beam toward the at least a second encounterparticipant.

Specifically, automated clinical documentation process 10 (via modularACD system 54 and/or audio recording system 104) may utilize one or moreof the discrete audio acquisition devices (e.g., audio acquisitiondevices 202, 204, 206, 208, 210, 212, 214, 216, 218) to form an audiorecording beam. For example, automated clinical documentation process 10may utilize audio acquisition device 210 to form audio recording beam220, thus enabling the capturing of audio (e.g., speech) produced byencounter participant 226 (as audio acquisition device 210 is pointed to(i.e., directed toward) encounter participant 226). Additionally,automated clinical documentation process 10 may utilize audioacquisition devices 204, 206 to form audio recording beam 222, thusenabling the capturing of audio (e.g., speech) produced by encounterparticipant 228 (as audio acquisition devices 204, 206 are pointed to(i.e., directed toward) encounter participant 228). Additionally,automated clinical documentation process 10 may utilize audioacquisition devices 212, 214 to form audio recording beam 224, thusenabling the capturing of audio (e.g., speech) produced by encounterparticipant 230 (as audio acquisition devices 212, 214 are pointed to(i.e., directed toward) encounter participant 230).

As there is a comparatively narrow angle of separation between audiorecording beam 220 and audio recording beam 224, automated clinicaldocumentation process 10 may process 804 the first audio encounterinformation (e.g., audio encounter information 106A from encounterparticipant 226) and the at least a second audio encounter information(e.g., audio encounter information 106C from encounter participant 230)to eliminate audio interference between the first audio encounterinformation (e.g., audio encounter information 106A) and the at least asecond audio encounter information (e.g., audio encounter information106C).

One example of such audio interference between the first audio encounterinformation (e.g., audio encounter information 106A) and the at least asecond audio encounter information (e.g., audio encounter information106C) may include but is not limited to crosstalk between the firstaudio encounter information (e.g., audio encounter information 106A) andthe at least a second audio encounter information (e.g., audio encounterinformation 106C). As is known in the art, crosstalk may occur when twoor more people are speaking simultaneously when e.g., speakers areinterrupting each other or during what may be referred to as “activelistening” (i.e., basically indicating attention and comprehension witha lot of utterances of e.g., “yes”, “got it” and “hmm”. Other commonsounds (e.g., heavy breathing and deep breathing) by the patient mayalso impact automated clinical documentation process 10 and may need tobe filtered out.

When processing 804 the first audio encounter information (e.g., audioencounter information 106A) and the at least a second audio encounterinformation (e.g., audio encounter information 106C) to eliminate audiointerference, automated clinical documentation process 10 may execute806 an echo cancellation process on the first audio encounterinformation (e.g., audio encounter information 106A) and the at least asecond audio encounter information (e.g., audio encounter information106C).

As is known in the art, echo cancellation is a method for improvingsignal quality by removing echo after it is already present. This methodmay be called acoustic echo suppression (AES) and acoustic echocancellation (AEC), and more rarely line echo cancellation (LEC). Insome cases, these terms are more precise, as there are various types andcauses of echo with unique characteristics, including acoustic echo(sounds from a loudspeaker being reflected and recorded by a microphone,which can vary substantially over time) and line echo (electricalimpulses caused by e.g., coupling between the sending and receivingwires, impedance mismatches, electrical reflections, etc., which variesmuch less than acoustic echo). Accordingly and in this configurations,such echo cancellation methodologies may be utilized to e.g., eliminatethe echo of a second speaker that appears in the audio recording beamsteered at a closely-positioned first speaker; while also eliminatingthe echo of the first speaker that appears in the audio recording beamsteered at the closely-positioned second speaker.

When processing 804 the first audio encounter information (e.g., audioencounter information 106A) and the at least a second audio encounterinformation (e.g., audio encounter information 106C) to eliminate audiointerference, automated clinical documentation process 10 may execute808 a blind source separation process on the first audio encounterinformation (e.g., audio encounter information 106A) and the at least asecond audio encounter information (e.g., audio encounter information106C).

As is known in the art, blind source separation is the separation of aset of source signals from a set of mixed signals, without the aid ofinformation (or with very little information) about the source signalsor the mixing process. This problem is in general highly underdeterminedbut useful solutions can be derived under a surprising variety ofconditions. Much of the early literature in this field focuses on theseparation of temporal signals such as audio. However, blind sourceseparation is now routinely performed on multidimensional data, such asimages and tensors that may involve no time dimension whatsoever. Sincethe chief difficulty of the problem is its underdetermination, methodsfor blind source separation generally seek to narrow the set of possiblesolutions in a way that is unlikely to exclude the desired solution. Inone approach, exemplified by principal and independent componentanalysis, one seeks source signals that are minimally correlated ormaximally independent in a probabilistic or information-theoretic sense.A second approach, exemplified by nonnegative matrix factorization, isto impose structural constraints on the source signals. These structuralconstraints may be derived from a generative model of the signal, butare more commonly heuristics justified by good empirical performance. Acommon theme in the second approach is to impose some kind oflow-complexity constraint on the signal, such as sparsity in some basisfor the signal space. This approach can be particularly effective if onerequires not the whole signal, but merely its most salient features.

As discussed above, modular ACD system 54 and/or audio recording system104 may be configured to utilize null-steering precoding to cancelinterference between speakers and/or noise.

Once processed 804, automated clinical documentation process 10 mayprocess 302 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to generateencounter transcript 234 and may process 304 at least a portion ofencounter transcript 234 to populate at least a portion of a medicalrecord (e.g., medical record 236) associated with the patient encounter(e.g., a visit to a doctor's office).

Invention #14

Automated clinical documentation process 10 may be configured to includea virtual assistant (e.g., virtual assistant 238) that is modular indesign. While this virtual assistant (e.g., virtual assistant 238) mayinclude only one persona (e.g., Nuance's Florence) for interacting withusers, this virtual assistant (e.g., virtual assistant 238) may includevarious functionality modules that may be configured to run in parallelwith each other and be added to (or removed from) the virtual assistant(e.g., virtual assistant 238) as needed

Accordingly and referring also to FIG. 17, automated clinicaldocumentation process 10 may be configured to obtain 850 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) via a compartmentalized virtual assistant(e.g., virtual assistant 238) during a patient encounter (e.g., a visitto a doctor's office), wherein the compartmentalized virtual assistant(e.g., virtual assistant 238) may include a core functionality module(e.g., core functionality module 244). An example of core functionalitymodule 244 may include but is not limited to a functionality module thatverbally interacts with an encounter participant (e.g., encounterparticipant 228) of the patient encounter (e.g., a visit to a doctor'soffice).

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office). For example and during a pre-visitportion of the patient encounter (e.g., a visit to a doctor's office),the patient may be directed to a patient “check-in” area withinmonitored space 130, wherein the compartmentalized virtual assistant(e.g., virtual assistant 238) may verbally interact with the encounterparticipant (e.g., encounter participant 228). Accordingly and withinthis “check-in” area, automated clinical documentation process 10 mayaudibly obtain 852 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) via thecompartmentalized virtual assistant (e.g., virtual assistant 238).

The compartmentalized virtual assistant (e.g., virtual assistant 238)may be configured to perform various different types of functionalityduring the patient encounter (e.g., a visit to a doctor's office). Forexample and during one portion of the patient encounter (e.g., a visitto a doctor's office), the compartmentalized virtual assistant (e.g.,virtual assistant 238) may require functionality to interact with amedical insurance coverage datasource to determine whether a particularmedication/medical procedure that was mentioned during a patientencounter (e.g., a visit to a doctor's office) is covered by the medicalinsurance plan of the patient. However, during other portions of thepatient encounter (e.g., a visit to a doctor's office), suchfunctionality may not be needed. Further and during other patientencounters, such functionality may not be needed at all.

Accordingly, automated clinical documentation process 10 may beconfigured to add 854 one or more additional functionalities to thecompartmentalized virtual assistant (e.g., virtual assistant 238) on anas-needed basis. Accordingly and if during the patient encounter (e.g.,a visit to a doctor's office), a certain functionality is needed, theappropriate functionality module (e.g., functionality module 246) may beadded 854 by automated clinical documentation process 10. When adding854 one or more additional functionalities to the compartmentalizedvirtual assistant (e.g., virtual assistant 238) on an as-needed basis,automated clinical documentation process 10 may load 856 one or moreadditional functionality modules (e.g., functionality module 246) forthe compartmentalized virtual assistant (e.g., virtual assistant 238)when needed to effectuate the additional functionalities. Accordinglyand by only loading 856 functionality modules (e.g., functionalitymodule 246) on an as-needed basis, modular ACD system 54 will not beunduly loaded, thus efficiently utilizing the system resources (e.g.,memory resources, compute resources, network resources, etc.) of modularACD system 54.

Further, automated clinical documentation process 10 may be configuredto remove 858 one or more existing functionalities from thecompartmentalized virtual assistant (e.g., virtual assistant 238) on anas-needed basis. Accordingly and when, during the patient encounter(e.g., a visit to a doctor's office) a certain functionality is nolonger needed, the appropriate functionality module (e.g., functionalitymodule 248) may be removed 858 by automated clinical documentationprocess 10. When removing 858 one or more existing functionalities fromthe compartmentalized virtual assistant (e.g., virtual assistant 238) onan as-needed basis, automated clinical documentation process 10 mayunload 860 one or more existing functionality modules (e.g.,functionality module 248) of the compartmentalized virtual assistant(e.g., virtual assistant 238) when no longer needed to effectuate theexisting functionalities. Accordingly and by unloading 860 functionalitymodules (e.g., functionality module 248) on an as-needed basis, modularACD system 54 will not be unduly loaded, thus efficiently utilizing thesystem resources (e.g., memory resources, compute resources, networkresources, etc.) of modular ACD system 54.

Once obtained 850, automated clinical documentation process 10 mayprocess 302 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to generateencounter transcript 234 and may process 304 at least a portion ofencounter transcript 234 to populate at least a portion of a medicalrecord (e.g., medical record 236) associated with the patient encounter(e.g., a visit to a doctor's office).

Invention #15

Automated clinical documentation process 10 may be configured to allowfor direct interaction of various functionality modules (e.g.,functionality modules 244, 246, 248) of the compartmentalized virtualassistant (e.g., virtual assistant 238), thus not requiring hub & spokeinteraction of these functionality modules.

Accordingly and referring also to FIG. 18, automated clinicaldocumentation process 10 may be configured to obtain 850 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) via the compartmentalized virtual assistant(e.g., virtual assistant 238) during a patient encounter (e.g., a visitto a doctor's office), wherein the compartmentalized virtual assistant(e.g., virtual assistant 238) may include a plurality of functionalitymodules (e.g., functionality modules 244, 246, 248).

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office). For example and during a pre-visitportion of the patient encounter (e.g., a visit to a doctor's office),the patient may be directed to a patient “check-in” area withinmonitored space 130, wherein the compartmentalized virtual assistant(e.g., virtual assistant 238) may verbally interact with the encounterparticipant (e.g., encounter participant 228). Accordingly and withinthis “check-in” area, automated clinical documentation process 10 mayaudibly obtain 852 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) via thecompartmentalized virtual assistant (e.g., virtual assistant 238).

When obtaining 850 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106), automatedclinical documentation process 10 may: obtain 306 encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) from a medical professional; obtain 308 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from a patient; and obtain 310 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from a third party. Further and whenobtaining 300 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106), automatedclinical documentation process 10 may obtain 300 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from previous (related or unrelated) patientencounters. For example, if the current patient encounter is actuallythe third visit that the patient is making concerning e.g., shortness ofbreath, the encounter information from the previous two visits (i.e.,the previous two patient encounters) may be highly-related and may beobtained 300 by automated clinical documentation process 10.

Once obtained 850, automated clinical documentation process 10 mayprocess 302 the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) to generateencounter transcript 234 and may process 304 at least a portion ofencounter transcript 234 to populate at least a portion of a medicalrecord (e.g., medical record 236) associated with the patient encounter(e.g., a visit to a doctor's office).

As will be discussed below in greater detail, automated clinicaldocumentation process 10 may process 900 at least a portion of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) via a first functionality module(e.g., functionality module 246) of the plurality of functionalitymodules (e.g., functionality modules 244, 246, 248) to generate a firstresult (e.g., result set 250). Automated clinical documentation process10 may provide 902 the first result (e.g., result set 250) to a secondfunctionality module (e.g., functionality module 244) of the pluralityof functionality modules (e.g., functionality modules 244, 246, 248).Automated clinical documentation process 10 may then process 904 thefirst result (e.g., result set 250) via the second functionality module(e.g., functionality module 244) to generate a second result (e.g.,result set 252), which may be provided to a third functionality module(e.g., functionality module 248) of the plurality of functionalitymodules (e.g., functionality modules 244, 246, 248) for processing.

As discussed above, automated clinical documentation process 10 may beconfigured to include a virtual assistant (e.g., virtual assistant 238)that is modular in design. So while this virtual assistant (e.g.,virtual assistant 238) may include only one persona (e.g., Nuance'sFlorence) for interacting with users, this virtual assistant (e.g.,virtual assistant 238) may include various functionality modules (e.g.,functionality modules 244, 246, 248) that may be configured to run inparallel with each other and effectuate different functionalities.

Accordingly, assume for this example that encounter participant 226 isgoing to prescribe an RA medication to encounter participant 228.Accordingly, the first functionality module (e.g., functionality module246) may be an insurance functionality module that is configured toprocess 900 this portion of the encounter information and interface witha medical insurance provider of encounter participant 228 to obtain alist of covered RA medications (e.g., first result 250). Functionalitymodule 246 may then provide 902 first result 250 to the secondfunctionality module (e.g., functionality module 244), which may be anadverse interaction functionality module that is configured to identifyany potential adverse interactions between the current medications ofencounter participant 228 and the approved RA medications defined withinfirst result 250.

Invention #16

Automated clinical documentation process 10 may be configured to utilizemachine vision (e.g., an RGB imaging system, an infrared imaging system,an ultraviolet imaging system, a SONAR imaging system, a laser imagingsystem, a RADAR imaging system and/or a thermal imaging system) torecord a visual representation (e.g., machine vision encounterinformation 102) of the patient encounter (in addition to recording anaudio representation (e.g., audio encounter information 106) of thepatient encounter), wherein automated clinical documentation process 10may index synchronize the visual representation (e.g., machine visionencounter information 102) and the audio representation (e.g., audioencounter information 106) of the patient encounter to produce anencounter recording.

Accordingly and referring also to FIG. 19, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office), wherein the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)includes machine vision encounter information and audio encounterinformation.

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Referring also to FIG. 20, automated clinical documentation process 10may temporarily-align 950 machine vision encounter information 102 andaudio encounter information 106 to produce temporarily-aligned encounterrecording 1000, which may be rendered 952 for the user of modular ACDsystem 54 via ACD media player 1002. Specifically and in one particularimplementation of ACD media player 1002, visual representation 1004 ofthe encounter information may allow the user of modular ACD system 54 toselect a particular portion of encounter recording 1000 for rendering,wherein automated clinical documentation process 10 may then render theappropriate portion of machine vision encounter information 102 andaudio encounter information 106 in a temporarily aligned fashion.

Invention #17

Automated clinical documentation process 10 may be configured toidentify (e.g., temporally & visually) the individual encounterparticipants (e.g., one or more of encounter participants 226, 228, 230)within an encounter recording of a patient encounter (e.g., a visit to adoctor's office).

Accordingly and referring also to FIG. 21, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office), wherein the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)includes machine vision encounter information and audio encounterinformation).

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), automated clinical documentation process 10 mayutilize 312 a virtual assistant (e.g., virtual assistant 238) to promptthe patient to provide at least a portion of the encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) during a pre-visit portion of the patient encounter(e.g., a visit to a doctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Automated clinical documentation process 10 may generate 1050 anencounter transcript based, at least in part, upon the first portion ofthe encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) and the at least a secondportion of the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106).

Automated clinical documentation process 10 may process 1052 theinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) information to: associate a first portion(e.g., machine vision encounter information 102A and/or audio encounterinformation 106A) of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) with afirst encounter participant (e.g., encounter participant 226), andassociate at least a second portion (e.g., machine vision encounterinformation 102B and/or audio encounter information 106B) of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) with at least a second encounterparticipant (e.g., encounter participant 228). The association of thesevarious encounter information portions with the various encounterparticipants may be accomplished in one or more of the methodologiesdescribed above (via the use of one or more of voice prints, faceprints, wearable tokens, utterances, interactions, etc.).

Once the above-described associations are made, automated clinicaldocumentation process 10 may render 1054 a visual representation (e.g.,visual representation 1004) of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106). As discussed above, visual representation 1004 of the encounterinformation may allow the user of modular ACD system 54 to select aparticular portion of encounter recording 1000 for rendering.

Automated clinical documentation process 10 may render 1056 a firstvisual representation (e.g., first visual representation 1006) of thefirst portion (e.g., machine vision encounter information 102A and/oraudio encounter information 106A) of the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) that is temporally-aligned with visual representation1004 of the encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106). Specifically,first visual representation 1006 may be visually indicative of theportions of the encounter information during which the first encounterparticipant (e.g., encounter participant 226) was speaking (asillustrated by darker grey portions versus lighter grey portions).

Further, automated clinical documentation process 10 may render 1058 atleast a second visual representation (e.g., second visual representation1008) of the at least a second portion (e.g., machine vision encounterinformation 102B and/or audio encounter information 106B) of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) that is temporally-aligned withvisual representation 1004 of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106). Specifically, second visual representation 1008 may be visuallyindicative of the portions of the encounter information during which thesecond encounter participant (e.g., encounter participant 228) wasspeaking (as illustrated by darker grey portions versus lighter greyportions).

Additionally, automated clinical documentation process 10 may beconfigured to allow the user of modular ACD system 54 to filter 1060 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) based upon one or more of thefirst visual representation (e.g., first visual representation 1006) andthe at least a second visual representation (e.g., second visualrepresentation 1008). For example, the user of modular ACD system 54 mayselect (e.g., via clicking) the appropriate visual representation (orappropriate visual representations) and automated clinical documentationprocess 10 may filter 1060 encounter recording 1000 based upon the userselections.

Invention #18

Automated clinical documentation process 10 may be configured toidentify (e.g., temporally & visually) the individual portions of anencounter recording of a patient encounter (e.g., a visit to a doctor'soffice).

Accordingly and referring also to FIG. 22, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office). When obtaining 300 encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), automated clinical documentation process 10 may:obtain 306 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) from a medicalprofessional (e.g., encounter participant 226); obtain 308 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from a patient (e.g., encounter participant228); and/or obtain 310 encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) from athird party (e.g., encounter participant 230). Further and whenobtaining 300 encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106), automatedclinical documentation process 10 may obtain 300 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) from previous (related or unrelated) patientencounters. For example, if the current patient encounter is actuallythe third visit that the patient is making concerning e.g., shortness ofbreath, the encounter information from the previous two visits (i.e.,the previous two patient encounters) may be highly-related and may beobtained 300 by automated clinical documentation process 10.

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Automated clinical documentation process 10 may process 1100 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) to: associate a first portion ofthe encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) with a first patientencounter portion (e.g., a pre-visit portion), and associate at least asecond portion of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) withat least a second patient encounter portion (e.g., the visit portion).The association of these various encounter information portions with thevarious patient encounter portions may be accomplished in one or more ofthe methodologies described above (via the use of one or more of voiceprints, face prints, wearable tokens, utterances, interactions, etc., aswell as the specific locations within monitored space 130 in which thevarious portions of the encounter information were generated).

Once the above-described associations are made, automated clinicaldocumentation process 10 may render 1102 a visual representation (e.g.,visual representation 1004) of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106). As discussed above, visual representation 1004 of the encounterinformation may allow the user of modular ACD system 54 to select aparticular portion of encounter recording 1000 for rendering.

Automated clinical documentation process 10 may render 1104 a firstvisual representation (e.g., first visual representation 1010) of thefirst portion (e.g., a pre-visit portion) of the encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) that is temporally-aligned with the visualrepresentation 1004 of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106).Specifically, first visual representation 1010 may be visuallyindicative of the pre-visit portion of the encounter information.

Automated clinical documentation process 10 may render 1106 at least asecond visual representation (e.g., second visual representation 1012)of the at least a second portion (e.g., a visit portion) of theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) that is temporally-aligned withvisual representation 1004 of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106). Specifically, second visual representation 1012 may be visuallyindicative of the visit portion of the encounter information.

Additionally, automated clinical documentation process 10 may beconfigured to allow the user of modular ACD system 54 to filter 1108 theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) based upon one or more of thefirst visual representation (e.g., first visual representation 1010) andthe at least a second visual representation (e.g., second visualrepresentation 1012). For example, the user of modular ACD system 54 mayselect (e.g., via clicking) the appropriate visual representation (orappropriate visual representations) and automated clinical documentationprocess 10 may filter 1108 encounter recording 1000 based upon the userselections.

Invention #19

Automated clinical documentation process 10 may be configured toreactively identify (at the request of a user) the various portions ofthe encounter recording that are indicative of a specific medicalcondition.

Accordingly and referring also to FIG. 23, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

As discussed above, since machine vision system 100 and audio recordingsystem 104 may be positioned throughout monitored space 130, all of theinteractions between medical professionals (e.g., encounter participant226), patients (e.g., encounter participant 228) and third parties(e.g., encounter participant 230) that occur during the patientencounter (e.g., encounter participant 228 visiting the doctor's office)within the monitored space (e.g., monitored space 130) of the clinicalenvironment (e.g., the doctor's office) may bemonitored/recorded/processed. Accordingly, a patient “check-in” areawithin monitored space 130 may be monitored to obtain encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) during this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice). Additionally and as discussed above, this pre-visit encounterinformation may be obtained via e.g., virtual assistant 238 before thepatient (e.g., encounter participant 228) has entered monitored space130. Further, various rooms within monitored space 130 may be monitoredto obtain encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during thesevarious portions of the patient encounter (e.g., while meeting with thedoctor, while vital signs and statistics are obtained, and while imagingis performed). Further, a patient “check-out” area within monitoredspace 130 may be monitored to obtain encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) during this post-visit portion of the patient encounter(e.g., encounter participant 228 visiting the doctor's office).Additionally and as discussed above, this post-visit encounterinformation may be obtained via e.g., virtual assistant 238 after thepatient (e.g., encounter participant 228) has left monitored space 130.Further and via machine vision encounter information 102, visual speechrecognition (via visual lip reading functionality) may be utilized byautomated clinical documentation process 10 to further effectuate thegathering of audio encounter information 106.

Accordingly, a complete recording of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) may begenerated, wherein this encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) may beprocessed 302 to generate an encounter transcript (e.g., encountertranscript 234) and at least a portion of this encounter transcript(e.g., encounter transcript 234) may be processed 304 to populate atleast a portion of a medical record (e.g., medical record 236)associated with the patient encounter (e.g., the visit to the doctor'soffice).

Automated clinical documentation process 10 may receive 1150 a requestfrom a user (e.g., a user of modular ACD system 54) concerning aspecific medical condition. When receiving 1150 a request from a user(e.g., a user of modular ACD system 54), automated clinicaldocumentation process 10 may: receive 1152 a verbal request from theuser (e.g., a user of modular ACD system 54) concerning the specificmedical condition; and/or receive 1154 a text-based request from theuser (e.g., a user of modular ACD system 54) concerning the specificmedical condition.

For example, assume that the user of modular ACD system 54 is encounterparticipant 226 (e.g., the doctor) who is examining encounterparticipant 228 (e.g., the patient). Accordingly, assume that encounterparticipant 226 (e.g., the doctor) is concerned that encounterparticipant 228 (e.g., the patient) may have diabetes. Accordingly,automated clinical documentation process 10 may receive 1150 a request(either verbal or text-based) from encounter participant 226 requestingthat automated clinical documentation process 10 identify any portionsof the encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) that may be indicative ofthe presence of diabetes with respect to encounter participant 228.

In response to receiving 1150 the request, automated clinicaldocumentation process 10 may process 1156 the encounter information(e.g., machine vision encounter information 102 and/or audio encounterinformation 106) to determine if the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) is indicative of this specific medical condition and togenerate a result set.

As discussed above, ACD compute system 12 may be configured to accessone or more datasources 118 (e.g., plurality of individual datasources120, 122, 124, 126, 128), examples of which may include but are notlimited to one or more of a user profile datasource, a voice printdatasource, a voice characteristics datasource (e.g., for adapting theautomated speech recognition models), a face print datasource, ahumanoid shape datasource, an utterance identifier datasource, awearable token identifier datasource, an interaction identifierdatasource, a medical conditions symptoms datasource, a prescriptionscompatibility datasource, a medical insurance coverage datasource, and ahome healthcare datasource.

Accordingly, automated clinical documentation process 10 may access theappropriate datasource to identify the symptoms for diabetes and maycompare those identified symptoms to the data included within theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106).

When processing 1156 the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106),automated clinical documentation process 10 may identify 1158 one ormore portions of the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) thatconcern the specific medical condition (in this example, diabetes), thusdefining one or more condition-related encounter portions. Automatedclinical documentation process 10 may then filter 1160 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) to highlight the one or morecondition-related encounter portions (thus defining result set 1016).

Automated clinical documentation process 10 may then provide 1162 resultset 1016 to the user (e.g., encounter participant 226). When providing1162 result set 1016 to the user (e.g., encounter participant 226),automated clinical documentation process 10 may render 1164 a visualrepresentation of result set 1016 for the user (e.g., encounterparticipant 226) and/or may render 1166 an audible representation ofresult set 1016 for the user (e.g., encounter participant 226).

For example, automated clinical documentation process 10 may provide1162 result set 1016 to encounter participant 226 in the manner shown inFIG. 20, wherein result set 1016 is visually indicative of the portionsof the encounter information that concern a specific medical condition(in this example, diabetes). Additionally/alternatively, automatedclinical documentation process 10 may provide 1162 result set 1016 as aprivate verbal message (e.g., that is rendered on an earbud worn byencounter participant 226) that provides the information requested byencounter participant 226 (e.g., “There are 23 portions of this patientencounter that indicate that this patient may have diabetes. An A1C testis recommended”.

Invention #20

Automated clinical documentation process 10 may be configured toproactively scan the entire encounter recording to identify any specificmedical conditions.

Accordingly and referring also to FIG. 24, automated clinicaldocumentation process 10 may be configured to obtain 300 encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) of a patient encounter (e.g., a visit to adoctor's office).

As discussed above, virtual assistant 238 may be configured to aidmedical professionals (e.g., doctors, nurses, physician's assistants,lab technicians, physical therapists, scribes (e.g., a transcriptionist)and/or staff members involved in the patient encounter) with thegathering of encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during variousportions of the patient encounter (e.g., encounter participant 228visiting the doctor's office).

Accordingly and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 312 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a pre-visit portion of the patient encounter (e.g., a visit to adoctor's office).

Further and when obtaining 300 encounter information (e.g., machinevision encounter information 102 and/or audio encounter information106), automated clinical documentation process 10 may utilize 314 avirtual assistant (e.g., virtual assistant 238) to prompt the patient toprovide at least a portion of the encounter information (e.g., machinevision encounter information 102 and/or audio encounter information 106)during a post-visit portion of the patient encounter (e.g., a visit to adoctor's office).

As discussed above, since machine vision system 100 and audio recordingsystem 104 may be positioned throughout monitored space 130, all of theinteractions between medical professionals (e.g., encounter participant226), patients (e.g., encounter participant 228) and third parties(e.g., encounter participant 230) that occur during the patientencounter (e.g., encounter participant 228 visiting the doctor's office)within the monitored space (e.g., monitored space 130) of the clinicalenvironment (e.g., the doctor's office) may bemonitored/recorded/processed. Accordingly, a patient “check-in” areawithin monitored space 130 may be monitored to obtain encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) during this pre-visit portion of the patientencounter (e.g., encounter participant 228 visiting the doctor'soffice). Additionally and as discussed above, this pre-visit encounterinformation may be obtained via e.g., virtual assistant 238 before thepatient (e.g., encounter participant 228) has entered monitored space130. Further, various rooms within monitored space 130 may be monitoredto obtain encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) during thesevarious portions of the patient encounter (e.g., while meeting with thedoctor, while vital signs and statistics are obtained, and while imagingis performed). Further, a patient “check-out” area within monitoredspace 130 may be monitored to obtain encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) during this post-visit portion of the patient encounter(e.g., encounter participant 228 visiting the doctor's office).Additionally and as discussed above, this post-visit encounterinformation may be obtained via e.g., virtual assistant 238 after thepatient (e.g., encounter participant 228) has left monitored space 130.Further and via machine vision encounter information 102, visual speechrecognition (via visual lip reading functionality) may be utilized byautomated clinical documentation process 10 to further effectuate thegathering of audio encounter information 106.

Accordingly, a complete recording of the patient encounter (e.g.,encounter participant 228 visiting the doctor's office) may begenerated, wherein this encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) may beprocessed 302 to generate an encounter transcript (e.g., encountertranscript 234) and at least a portion of this encounter transcript(e.g., encounter transcript 234) may be processed 304 to populate atleast a portion of a medical record (e.g., medical record 236)associated with the patient encounter (e.g., the visit to the doctor'soffice).

Automated clinical documentation process 10 may proactively process 1200the encounter information (e.g., machine vision encounter information102 and/or audio encounter information 106) to determine if theencounter information (e.g., machine vision encounter information 102and/or audio encounter information 106) is indicative of one or moremedical conditions and to generate one or more result sets. For example,automated clinical documentation process 10 may continuously (orregularly) scan the encounter information (e.g., machine visionencounter information 102 and/or audio encounter information 106) todetermine if this encounter information (e.g., machine vision encounterinformation 102 and/or audio encounter information 106) is indicative ofone or more medical conditions.

As discussed above, ACD compute system 12 may be configured to accessone or more datasources 118 (e.g., plurality of individual datasources120, 122, 124, 126, 128), examples of which may include but are notlimited to one or more of a user profile datasource, a voice printdatasource, a voice characteristics datasource (e.g., for adapting theautomated speech recognition models), a face print datasource, ahumanoid shape datasource, an utterance identifier datasource, awearable token identifier datasource, an interaction identifierdatasource, a medical conditions symptoms datasource, a prescriptionscompatibility datasource, a medical insurance coverage datasource, and ahome healthcare datasource. Accordingly, automated clinicaldocumentation process 10 may proactively access the appropriatedatasource to identify the symptoms of various medical conditions (e.g.,diabetes) and may compare the identified symptoms of the various medicalconditions to the data included within the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106).

When proactively processing 1200 the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106), automated clinical documentation process 10 mayidentify 1202 one or more portions of the encounter information (e.g.,machine vision encounter information 102 and/or audio encounterinformation 106) that concern one or more medical conditions, thusdefining one or more condition-related encounter portions. Automatedclinical documentation process 10 may then filter 1204 the encounterinformation (e.g., machine vision encounter information 102 and/or audioencounter information 106) to highlight the one or morecondition-related encounter portions.

Automated clinical documentation process 10 may provide 1206 the one ormore result sets (e.g., result set 1016) to the user (e.g., encounterparticipant 226). The one or more result sets (e.g., result set 1016)may include: a first result set indicative of a first medical condition;and at least a second result set indicative of at least a second medicalcondition. For example, while FIG. 20 illustrates a single/consolidatedresult set, this is for illustrative purpose only and is not intended tobe a limitation of this disclosure, as other configurations are possibleand are considered to be within the scope of this disclosure. Forexample, assume that automated clinical documentation process 10 found(within the encounter information) data that indicates that encounterparticipant 228 may have diabetes and may have heart disease.Accordingly and in such a situation, result set 1016 may include a firstresult set that is indicative of diabetes and a second result setindicative of heart disease.

When providing 1206 the one or more result sets (e.g., result set 1016)to the user (e.g., encounter participant 226), automated clinicaldocumentation process 10 may render 1208 a visual representation of theone or more result sets (e.g., result set 1016) for the user (e.g.,encounter participant 226) and/or may render 1210 an audiblerepresentation of the one or more result sets (e.g., result set 1016)for the user (e.g., encounter participant 226).

For example, automated clinical documentation process 10 may provide1206 result set 1016 to encounter participant 226 in the manner shown inFIG. 20, wherein result set 1016 is visually indicative of the portionsof the encounter information that concern a specific medical condition(in this example, diabetes and/or heart disease).Additionally/alternatively, automated clinical documentation process 10may provide 1206 result set 1016 as a private verbal message (e.g., thatis rendered on an earbud worn by encounter participant 226) thatprovides the information requested by encounter participant 226 (e.g.,“There are 23 portions of this patient encounter that indicate that thispatient may have diabetes. An A1C test is recommended There are 16portions of this patient encounter that indicate that this patient mayhave heart disease. An echocardiogram is recommended.”

General:

As will be appreciated by one skilled in the art, the present disclosuremay be embodied as a method, a system, or a computer program product.Accordingly, the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present disclosure may take the form of a computer program producton a computer-usable storage medium having computer-usable program codeembodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. More specific examples (a non-exhaustive list) ofthe computer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a transmission media such as those supportingthe Internet or an intranet, or a magnetic storage device. Thecomputer-usable or computer-readable medium may also be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via, for instance, optical scanning of thepaper or other medium, then compiled, interpreted, or otherwiseprocessed in a suitable manner, if necessary, and then stored in acomputer memory. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentdisclosure may be written in an object oriented programming languagesuch as Java, Smalltalk, C++ or the like. However, the computer programcode for carrying out operations of the present disclosure may also bewritten in conventional procedural programming languages, such as the“C” programming language or similar programming languages. The programcode may execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network/a widearea network/the Internet (e.g., network 14).

The present disclosure is described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the disclosure. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, may be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer/special purposecomputer/other programmable data processing apparatus, such that theinstructions, which execute via the processor of the computer or otherprogrammable data processing apparatus, create means for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

These computer program instructions may also be stored in acomputer-readable memory that may direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures may illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustrations, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

A number of implementations have been described. Having thus describedthe disclosure of the present application in detail and by reference toembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of thedisclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method for trackingencounter participants, executed on a computing device, comprising:obtaining encounter information of a patient encounter, wherein theencounter information includes machine vision encounter informationobtained via one or more machine vision systems; processing the machinevision encounter information to recognize one or more humanoid shapeswithin a monitored space, wherein processing the machine visionencounter information includes identifying movement of a first humanoidshape of the one or more humanoid shapes, and recognizing a secondhumanoid shape within the monitored space; obtaining audio encounterinformation of the encounter information for at least one of the firstand second humanoid shapes; processing the encounter information togenerate at least a portion of an encounter transcript; and processingat least the portion of the encounter transcript to populate at least aportion of a medical record associated with the patient encounter. 2.The computer-implemented method of claim 1 wherein processing themachine vision encounter information to identify one or more humanoidshapes includes: tracking the movement of the one or more humanoidshapes within a monitored space.
 3. The computer-implemented method ofclaim 2 wherein tracking the movement of the one or more humanoid shapeswithin the monitored space includes: adding the second humanoid shape tothe one or more humanoid shapes when the second humanoid shape entersthe monitored space.
 4. The computer-implemented method of claim 2wherein tracking the movement of the one or more humanoid shapes withinthe monitored space includes: removing an existing humanoid shape fromthe one or more humanoid shapes when the existing humanoid shape leavesthe monitored space.
 5. The computer-implemented method of claim 1further comprising: steering one or more audio recording beams towardthe one or more humanoid shapes to capture the audio encounterinformation, wherein the audio encounter information is included withinthe encounter information.
 6. The computer-implemented method of claim 1wherein the one or more machine vision systems includes one or more of:an RGB imaging system; an infrared imaging system; an ultravioletimaging system; a laser imaging system; an X-ray imaging system; a SONARimaging system; a RADAR imaging system; and a thermal imaging system. 7.A computer program product residing on a computer readable medium havinga plurality of instructions stored thereon which, when executed by aprocessor, cause the processor to perform operations comprising:obtaining encounter information of a patient encounter, wherein theencounter information includes machine vision encounter informationobtained via one or more machine vision systems; processing the machinevision encounter information to recognize one or more humanoid shapeswithin a monitored space, wherein processing the machine visionencounter information includes identifying movement of a first humanoidshape of the one or more humanoid shapes, and recognizing a secondhumanoid shape within the monitored space; obtaining audio encounterinformation of the encounter information for at least one of the firstand second humanoid shapes; processing the encounter information togenerate at least a portion of an encounter transcript; and processingat least the portion of the encounter transcript to populate at least aportion of a medical record associated with the patient encounter. 8.The computer program product of claim 7 wherein processing the machinevision encounter information to identify one or more humanoid shapesincludes: tracking the movement of the one or more humanoid shapeswithin a monitored space.
 9. The computer program product of claim 8wherein tracking the movement of the one or more humanoid shapes withinthe monitored space includes: adding the second humanoid shape to theone or more humanoid shapes when the second humanoid shape enters themonitored space.
 10. The computer program product of claim 8 whereintracking the movement of the one or more humanoid shapes within themonitored space includes: removing an existing humanoid shape from theone or more humanoid shapes when the existing humanoid shape leaves themonitored space.
 11. The computer program product of claim 7 furthercomprising: steering one or more audio recording beams toward the one ormore humanoid shapes to capture the audio encounter information, whereinthe audio encounter information is included within the encounterinformation.
 12. The computer program product of claim 7 wherein the oneor more machine vision systems includes one or more of: an RGB imagingsystem; an infrared imaging system; an ultraviolet imaging system; alaser imaging system; an X-ray imaging system; a SONAR imaging system; aRADAR imaging system; and a thermal imaging system.
 13. A computingsystem including a processor and memory configured to perform operationscomprising: obtaining encounter information of a patient encounter,wherein the encounter information includes machine vision encounterinformation obtained via one or more machine vision systems; processingthe machine vision encounter information to recognize one or morehumanoid shapes within a monitored space, wherein processing the machinevision encounter information includes identifying movement of a firsthumanoid shape of the one or more humanoid shapes, and recognizing asecond humanoid shape within the monitored space; obtaining audioencounter information of the encounter information for at least one ofthe first and second humanoid shapes; processing the encounterinformation to generate at least a portion of an encounter transcript;and processing at least the portion of the encounter transcript topopulate at least a portion of a medical record associated with thepatient encounter.
 14. The computing system of claim 13 whereinprocessing the machine vision encounter information to identify one ormore humanoid shapes includes: tracking the movement of the one or morehumanoid shapes within a monitored space.
 15. The computing system ofclaim 14 wherein tracking the movement of the one or more humanoidshapes within the monitored space includes: adding the second humanoidshape to the one or more humanoid shapes when the second humanoid shapeenters the monitored space.
 16. The computing system of claim 14 whereintracking the movement of the one or more humanoid shapes within themonitored space includes: removing an existing humanoid shape from theone or more humanoid shapes when the existing humanoid shape leaves themonitored space.
 17. The computing system of claim 13 furthercomprising: steering one or more audio recording beams toward the one ormore humanoid shapes to capture the audio encounter information, whereinthe audio encounter information is included within the encounterinformation.
 18. The computing system of claim 13 wherein the one ormore machine vision systems includes one or more of: an RGB imagingsystem; an infrared imaging system; an ultraviolet imaging system; alaser imaging system; an X-ray imaging system; a SONAR imaging system; aRADAR imaging system; and a thermal imaging system.